数据挖掘技术中英文对照外文翻译文献
数据挖掘导论英文版
数据挖掘导论英文版Data Mining IntroductionData mining is the process of extracting valuable insights and patterns from large datasets. It involves the application of various techniques and algorithms to uncover hidden relationships, trends, and anomalies that can be used to inform decision-making and drive business success. In today's data-driven world, the ability to effectively harness the power of data has become a critical competitive advantage for organizations across a wide range of industries.One of the key strengths of data mining is its versatility. It can be applied to a wide range of domains, from marketing and finance to healthcare and scientific research. In the marketing realm, for example, data mining can be used to analyze customer behavior, identify target segments, and develop personalized marketing strategies. In the financial sector, data mining can be leveraged to detect fraud, assess credit risk, and optimize investment portfolios.At the heart of data mining lies a diverse set of techniques and algorithms. These include supervised learning methods, such asregression and classification, which can be used to predict outcomes based on known patterns in the data. Unsupervised learning techniques, such as clustering and association rule mining, can be employed to uncover hidden structures and relationships within datasets. Additionally, advanced algorithms like neural networks and decision trees have proven to be highly effective in tackling complex, non-linear problems.The process of data mining typically involves several key steps, each of which plays a crucial role in extracting meaningful insights from the data. The first step is data preparation, which involves cleaning, transforming, and integrating the raw data into a format that can be effectively analyzed. This step is particularly important, as the quality and accuracy of the input data can significantly impact the reliability of the final results.Once the data is prepared, the next step is to select the appropriate data mining techniques and algorithms to apply. This requires a deep understanding of the problem at hand, as well as the strengths and limitations of the available tools. Depending on the specific goals of the analysis, the data mining practitioner may choose to employ a combination of techniques, each of which can provide unique insights and perspectives.The next phase is the actual data mining process, where the selectedalgorithms are applied to the prepared data. This can involve complex mathematical and statistical calculations, as well as the use of specialized software and computing resources. The results of this process may include the identification of patterns, trends, and relationships within the data, as well as the development of predictive models and other data-driven insights.Once the data mining process is complete, the final step is to interpret and communicate the findings. This involves translating the technical results into actionable insights that can be easily understood by stakeholders, such as business leaders, policymakers, or scientific researchers. Effective communication of data mining results is crucial, as it enables decision-makers to make informed choices and take appropriate actions based on the insights gained.One of the most exciting aspects of data mining is its continuous evolution and the emergence of new techniques and technologies. As the volume and complexity of data continue to grow, the need for more sophisticated and powerful data mining tools and algorithms has become increasingly pressing. Advances in areas such as machine learning, deep learning, and big data processing have opened up new frontiers in data mining, enabling practitioners to tackle increasingly complex problems and extract even more valuable insights from the data.In conclusion, data mining is a powerful and versatile tool that has the potential to transform the way we approach a wide range of challenges and opportunities. By leveraging the power of data and the latest analytical techniques, organizations can gain a deeper understanding of their operations, customers, and markets, and make more informed, data-driven decisions that drive sustainable growth and success. As the field of data mining continues to evolve, it is clear that it will play an increasingly crucial role in shaping the future of business, science, and society as a whole.。
数据挖掘_Data sets from the book The Analysis of Time Series(《The Analysis of Time Series》中的数据
Data sets from the book "The Analysis of Time Series"(《The Analysis of Time Series》中的数据)数据摘要：This file is a text file giving details about the time series analysed in 'The Analysis of Time Series' by Chris Chatfield. The 5th edn was published in 1996 and the 6th edn in 2003.中文关键词：数据挖掘,时间序列,分析,Chris Chatfield,英文关键词：Data mining,Time series,Analysis,Chris Chatfield,数据格式：TEXT数据用途：The data can be used for data mining and analysis.数据详细介绍：Data sets from the book "The Analysisof Time Series"∙AbstractThis file is a text file giving details about the time series analysed in 'The Analysis of Time Series' by Chris Chatfield. The 5th edn was published in 1996 and the 6th edn in 2003.∙Data DescriptionAn individual series can readily be abstracted from this file.Figure 1.1 - the Beveridge wheat price index series is availablein many places, such as /depts/maths/data/ts/Figure 1.2Average air temperature (deg C) in Recife in successive months for 1953-1962.Figure 1.3 - The Chatfield-Prothero data for Company X.Monthly sales for January 1965 to November 1971.From Series A, 1973, p. 295-.Includes the last 6 observations from the follow-up paper replying tothe Box and Jenkins paper - see p. 251 of Series A 1973.Example 5.1 in 6th edn.Values are coded - hence no unit of measurement.Time period from Aug 1988 to July 1992 - 4 years.Figure 11.1 - the sunspots data is available in many places but isrepeated here for convenience.Monthly sunspots numbers from 1749 to 1983.Table D.2 in 5th edn and 14.2 in 6th edn.Yield (%) on British short term government securities in successive monthsfrom about 1950 to about 1971.Figure D.2 in 5th edn or Fig 14.2 in 6th edn.Monthly totals of international airline passengers (in thousands) for 1949-1960.This data is readily available elsewhere but is repeated here for convenience.Reference'The Analysis of Time Series' by Chris Chatfield. The 5th edn was published in 1996 and the 6th edn in 2003.数据预览：点此下载完整数据集。
大数据外文翻译参考文献综述
大数据外文翻译参考文献综述(文档含中英文对照即英文原文和中文翻译)原文：Data Mining and Data PublishingData mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the partyrunning the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model [1] which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.Although data mining is potentially useful, many data holders are reluctant to provide their data for data mining for the fear of violating individual privacy. In recent years, study has been made to ensure that the sensitive information of individuals cannot be identified easily.Anonymity Models, k-anonymization techniques have been the focus of intense research in the last few years. In order to ensure anonymization of data while at the same time minimizing the informationloss resulting from data modifications, everal extending models are proposed, which are discussed as follows.1.k-Anonymityk-anonymity is one of the most classic models, which technique that prevents joining attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. In the k-anonymous tables, a data set is k-anonymous (k ≥ 1) if each record in the data set is in- distinguishable from at least (k . 1) other records within the same data set. The larger the value of k, the better the privacy is protected. k-anonymity can ensure that individuals cannot be uniquely identified by linking attacks.2. Extending ModelsSince k-anonymity does not provide sufficient protection against attribute disclosure. The notion of l-diversity attempts to solve this problem by requiring that each equivalence class has at least l well-represented value for each sensitive attribute. The technology of l-diversity has some advantages than k-anonymity. Because k-anonymity dataset permits strong attacks due to lack of diversity in the sensitive attributes. In this model, an equivalence class is said to have l-diversity if there are at least l well-represented value for the sensitive attribute. Because there are semantic relationships among the attribute values, and different values have very different levels of sensitivity. Afteranonymization, in any equivalence class, the frequency (in fraction) of a sensitive value is no more than α.3. Related Research AreasSeveral polls show that the public has an in- creased sense of privacy loss. Since data mining is often a key component of information systems, homeland security systems, and monitoring and surveillance systems, it gives a wrong impression that data mining is a technique for privacy intrusion. This lack of trust has become an obstacle to the benefit of the technology. For example, the potentially beneficial data mining re- search project, Terrorism Information Awareness (TIA), was terminated by the US Congress due to its controversial procedures of collecting, sharing, and analyzing the trails left by individuals. Motivated by the privacy concerns on data mining tools, a research area called privacy-reserving data mining (PPDM) emerged in 2000. The initial idea of PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. The solutions were often tightly coupled with the data mining algorithms under consideration. In contrast, privacy-preserving data publishing (PPDP) may not necessarily tie to a specific data mining task, and the data mining task is sometimes unknown at the time of data publishing. Furthermore, some PPDP solutions emphasize preserving the datatruthfulness at the record level, but PPDM solutions often do not preserve such property. PPDP Differs from PPDM in Several Major Ways as Follows ：1) PPDP focuses on techniques for publishing data, not techniques for data mining. In fact, it is expected that standard data mining techniques are applied on the published data. In contrast, the data holder in PPDM needs to randomize the data in such a way that data mining results can be recovered from the randomized data. To do so, the data holder must understand the data mining tasks and algorithms involved. This level of involvement is not expected of the data holder in PPDP who usually is not an expert in data mining.2) Both randomization and encryption do not preserve the truthfulness of values at the record level; therefore, the released data are basically meaningless to the recipients. In such a case, the data holder in PPDM may consider releasing the data mining results rather than the scrambled data.3) PPDP primarily “anonymizes” the data by hiding the identity of record owners, whereas PPDM seeks to directly hide the sensitive data. Excellent surveys and books in randomization and cryptographic techniques for PPDM can be found in the existing literature. A family of research work called privacy-preserving distributed data mining (PPDDM) aims at performing some data mining task on a set of private databasesowned by different parties. It follows the principle of Secure Multiparty Computation (SMC), and prohibits any data sharing other than the final data mining result. Clifton et al. present a suite of SMC operations, like secure sum, secure set union, secure size of set intersection, and scalar product, that are useful for many data mining tasks. In contrast, PPDP does not perform the actual data mining task, but concerns with how to publish the data so that the anonymous data are useful for data mining. We can say that PPDP protects privacy at the data level while PPDDM protects privacy at the process level. They address different privacy models and data mining scenarios. In the field of statistical disclosure control (SDC), the research works focus on privacy-preserving publishing methods for statistical tables. SDC focuses on three types of disclosures, namely identity disclosure, attribute disclosure, and inferential disclosure. Identity disclosure occurs if an adversary can identify a respondent from the published data. Revealing that an individual is a respondent of a data collection may or may not violate confidentiality requirements. Attribute disclosure occurs when confidential information about a respondent is revealed and can be attributed to the respondent. Attribute disclosure is the primary concern of most statistical agencies in deciding whether to publish tabular data. Inferential disclosure occurs when individual information can be inferred with high confidence from statistical information of the published data.Some other works of SDC focus on the study of the non-interactive query model, in which the data recipients can submit one query to the system. This type of non-interactive query model may not fully address the information needs of data recipients because, in some cases, it is very difficult for a data recipient to accurately construct a query for a data mining task in one shot. Consequently, there are a series of studies on the interactive query model, in which the data recipients, including adversaries, can submit a sequence of queries based on previously received query results. The database server is responsible to keep track of all queries of each user and determine whether or not the currently received query has violated the privacy requirement with respect to all previous queries. One limitation of any interactive privacy-preserving query system is that it can only answer a sublinear number of queries in total; otherwise, an adversary (or a group of corrupted data recipients) will be able to reconstruct all but 1 . o(1) fraction of the original data, which is a very strong violation of privacy. When the maximum number of queries is reached, the query service must be closed to avoid privacy leak. In the case of the non-interactive query model, the adversary can issue only one query and, therefore, the non-interactive query model cannot achieve the same degree of privacy defined by Introduction the interactive model. One may consider that privacy-reserving data publishing is a special case of the non-interactivequery model.This paper presents a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explains their effects on Data Privacy. k-anonymity is used for security of respondents identity and decreases linking attack in the case of homogeneity attack a simple k-anonymity model fails and we need a concept which prevent from this attack solution is l-diversity. All tuples are arranged in well represented form and adversary will divert to l places or on l sensitive attributes. l-diversity limits in case of background knowledge attack because no one predicts knowledge level of an adversary. It is observe that using generalization and suppression we also apply these techniques on those attributes which doesn’t need th is extent of privacy and this leads to reduce the precision of publishing table. e-NSTAM (extended Sensitive Tuples Anonymity Method) is applied on sensitive tuples only and reduces information loss, this method also fails in the case of multiple sensitive tuples.Generalization with suppression is also the causes of data lose because suppression emphasize on not releasing values which are not suited for k factor. Future works in this front can include defining a new privacy measure along with l-diversity for multiple sensitive attribute and we will focus to generalize attributes without suppression using other techniques which are used to achieve k-anonymity because suppression leads to reduce the precision ofpublishing table.译文：数据挖掘和数据发布数据挖掘中提取出大量有趣的模式从大量的数据或知识。
大数据外文翻译参考文献综述
大数据外文翻译参考文献综述(文档含中英文对照即英文原文和中文翻译)原文：Data Mining and Data PublishingData mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the partyrunning the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model [1] which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.Although data mining is potentially useful, many data holders are reluctant to provide their data for data mining for the fear of violating individual privacy. In recent years, study has been made to ensure that the sensitive information of individuals cannot be identified easily.Anonymity Models, k-anonymization techniques have been the focus of intense research in the last few years. In order to ensure anonymization of data while at the same time minimizing the informationloss resulting from data modifications, everal extending models are proposed, which are discussed as follows.1.k-Anonymityk-anonymity is one of the most classic models, which technique that prevents joining attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. In the k-anonymous tables, a data set is k-anonymous (k ≥ 1) if each record in the data set is in- distinguishable from at least (k . 1) other records within the same data set. The larger the value of k, the better the privacy is protected. k-anonymity can ensure that individuals cannot be uniquely identified by linking attacks.2. Extending ModelsSince k-anonymity does not provide sufficient protection against attribute disclosure. The notion of l-diversity attempts to solve this problem by requiring that each equivalence class has at least l well-represented value for each sensitive attribute. The technology of l-diversity has some advantages than k-anonymity. Because k-anonymity dataset permits strong attacks due to lack of diversity in the sensitive attributes. In this model, an equivalence class is said to have l-diversity if there are at least l well-represented value for the sensitive attribute. Because there are semantic relationships among the attribute values, and different values have very different levels of sensitivity. Afteranonymization, in any equivalence class, the frequency (in fraction) of a sensitive value is no more than α.3. Related Research AreasSeveral polls show that the public has an in- creased sense of privacy loss. Since data mining is often a key component of information systems, homeland security systems, and monitoring and surveillance systems, it gives a wrong impression that data mining is a technique for privacy intrusion. This lack of trust has become an obstacle to the benefit of the technology. For example, the potentially beneficial data mining re- search project, Terrorism Information Awareness (TIA), was terminated by the US Congress due to its controversial procedures of collecting, sharing, and analyzing the trails left by individuals. Motivated by the privacy concerns on data mining tools, a research area called privacy-reserving data mining (PPDM) emerged in 2000. The initial idea of PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. The solutions were often tightly coupled with the data mining algorithms under consideration. In contrast, privacy-preserving data publishing (PPDP) may not necessarily tie to a specific data mining task, and the data mining task is sometimes unknown at the time of data publishing. Furthermore, some PPDP solutions emphasize preserving the datatruthfulness at the record level, but PPDM solutions often do not preserve such property. PPDP Differs from PPDM in Several Major Ways as Follows ：1) PPDP focuses on techniques for publishing data, not techniques for data mining. In fact, it is expected that standard data mining techniques are applied on the published data. In contrast, the data holder in PPDM needs to randomize the data in such a way that data mining results can be recovered from the randomized data. To do so, the data holder must understand the data mining tasks and algorithms involved. This level of involvement is not expected of the data holder in PPDP who usually is not an expert in data mining.2) Both randomization and encryption do not preserve the truthfulness of values at the record level; therefore, the released data are basically meaningless to the recipients. In such a case, the data holder in PPDM may consider releasing the data mining results rather than the scrambled data.3) PPDP primarily “anonymizes” the data by hiding the identity of record owners, whereas PPDM seeks to directly hide the sensitive data. Excellent surveys and books in randomization and cryptographic techniques for PPDM can be found in the existing literature. A family of research work called privacy-preserving distributed data mining (PPDDM) aims at performing some data mining task on a set of private databasesowned by different parties. It follows the principle of Secure Multiparty Computation (SMC), and prohibits any data sharing other than the final data mining result. Clifton et al. present a suite of SMC operations, like secure sum, secure set union, secure size of set intersection, and scalar product, that are useful for many data mining tasks. In contrast, PPDP does not perform the actual data mining task, but concerns with how to publish the data so that the anonymous data are useful for data mining. We can say that PPDP protects privacy at the data level while PPDDM protects privacy at the process level. They address different privacy models and data mining scenarios. In the field of statistical disclosure control (SDC), the research works focus on privacy-preserving publishing methods for statistical tables. SDC focuses on three types of disclosures, namely identity disclosure, attribute disclosure, and inferential disclosure. Identity disclosure occurs if an adversary can identify a respondent from the published data. Revealing that an individual is a respondent of a data collection may or may not violate confidentiality requirements. Attribute disclosure occurs when confidential information about a respondent is revealed and can be attributed to the respondent. Attribute disclosure is the primary concern of most statistical agencies in deciding whether to publish tabular data. Inferential disclosure occurs when individual information can be inferred with high confidence from statistical information of the published data.Some other works of SDC focus on the study of the non-interactive query model, in which the data recipients can submit one query to the system. This type of non-interactive query model may not fully address the information needs of data recipients because, in some cases, it is very difficult for a data recipient to accurately construct a query for a data mining task in one shot. Consequently, there are a series of studies on the interactive query model, in which the data recipients, including adversaries, can submit a sequence of queries based on previously received query results. The database server is responsible to keep track of all queries of each user and determine whether or not the currently received query has violated the privacy requirement with respect to all previous queries. One limitation of any interactive privacy-preserving query system is that it can only answer a sublinear number of queries in total; otherwise, an adversary (or a group of corrupted data recipients) will be able to reconstruct all but 1 . o(1) fraction of the original data, which is a very strong violation of privacy. When the maximum number of queries is reached, the query service must be closed to avoid privacy leak. In the case of the non-interactive query model, the adversary can issue only one query and, therefore, the non-interactive query model cannot achieve the same degree of privacy defined by Introduction the interactive model. One may consider that privacy-reserving data publishing is a special case of the non-interactivequery model.This paper presents a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explains their effects on Data Privacy. k-anonymity is used for security of respondents identity and decreases linking attack in the case of homogeneity attack a simple k-anonymity model fails and we need a concept which prevent from this attack solution is l-diversity. All tuples are arranged in well represented form and adversary will divert to l places or on l sensitive attributes. l-diversity limits in case of background knowledge attack because no one predicts knowledge level of an adversary. It is observe that using generalization and suppression we also apply these techniques on those attributes which doesn’t need th is extent of privacy and this leads to reduce the precision of publishing table. e-NSTAM (extended Sensitive Tuples Anonymity Method) is applied on sensitive tuples only and reduces information loss, this method also fails in the case of multiple sensitive tuples.Generalization with suppression is also the causes of data lose because suppression emphasize on not releasing values which are not suited for k factor. Future works in this front can include defining a new privacy measure along with l-diversity for multiple sensitive attribute and we will focus to generalize attributes without suppression using other techniques which are used to achieve k-anonymity because suppression leads to reduce the precision ofpublishing table.译文：数据挖掘和数据发布数据挖掘中提取出大量有趣的模式从大量的数据或知识。
数据挖掘技术毕业论文中英文资料对照外文翻译文献综述
数据挖掘技术毕业论文中英文资料对照外文翻译文献综述数据挖掘技术简介中英文资料对照外文翻译文献综述英文原文Introduction to Data MiningAbstract:Microsoft® SQL Server™ 2005 provides an integrated environment for creating and working with data mining models. This tutorial uses four scenarios, targeted mailing, forecasting, market basket, and sequence clustering, to demonstrate how to use the mining model algorithms, mining model viewers, and data mining tools that are included in this release of SQL Server.IntroductionThe data mining tutorial is designed to walk you through the process of creating data mining models in Microsoft SQL Server 2005. The data mining algorithms and tools in SQL Server 2005 make it easy to build a comprehensive solution for a variety of projects, including market basket analysis, forecasting analysis, and targeted mailing analysis. The scenarios for these solutions are explained in greater detail later in the tutorial.The most visible components in SQL Server 2005 are the workspaces that you use to create and work with data mining models. The online analytical processing (OLAP) and data mining tools are consolidated into two working environments: Business Intelligence Development Studio and SQL Server Management Studio. Using Business Intelligence Development Studio, you can develop an Analysis Services project disconnected from the server. When the project is ready, you can deploy it to the server. You can also work directly against the server. The main function of SQL Server Management Studio is to manage the server. Each environment is described in more detail later in this introduction. For more information on choosing between the two environments, see "Choosing Between SQL Server Management Studio and Business Intelligence Development Studio" in SQL Server Books Online.All of the data mining tools exist in the data mining editor. Using the editor you can manage mining models, create new models, view models, compare models, and create predictions basedon existing models.After you build a mining model, you will want to explore it, looking for interesting patterns and rules. Each mining model viewer in the editor is customized to explore models built with a specific algorithm. For more information about the viewers, see "Viewing a Data Mining Model" in SQL Server Books Online.Often your project will contain several mining models, so before you can use a model to create predictions, you need to be able to determine which model is the most accurate. For this reason, the editor contains a model comparison tool called the Mining Accuracy Chart tab. Using this tool you can compare the predictive accuracy of your models and determine the best model.To create predictions, you will use the Data Mining Extensions (DMX) language. DMX extends SQL, containing commands to create, modify, and predict against mining models. For more information about DMX, see "Data Mining Extensions (DMX) Reference" in SQL Server Books Online. Because creating a prediction can be complicated, the data mining editor contains a tool called Prediction Query Builder, which allows you to build queries using a graphical interface. You can also view the DMX code that is generated by the query builder.Just as important as the tools that you use to work with and create data mining models are the mechanics by which they are created. The key to creating a mining model is the data mining algorithm. The algorithm finds patterns in the data that you pass it, and it translates them into a mining model — it is the engine behind the process.Some of the most important steps in creating a data mining solution are consolidating, cleaning, and preparing the data to be used to create the mining models. SQL Server 2005 includes the Data Transformation Services (DTS) working environment, which contains tools that you can use to clean, validate, and prepare your data. For more information on using DTS in conjunction with a data mining solution, see "DTS Data Mining Tasks and Transformations" in SQL Server Books Online.In order to demonstrate the SQL Server data mining features, this tutorial uses a new sample database called AdventureWorksDW. The database is included with SQL Server 2005, and it supports OLAP and data mining functionality. In order to make the sample database available, you need to select the sample database at the installation time in the “Advanced” dialog for component selection.Adventure WorksAdventureWorksDW is based on a fictional bicycle manufacturing company named Adventure Works Cycles. Adventure Works produces and distributes metal and composite bicycles to North American, European, and Asian commercial markets. The base of operations is located in Bothell, Washington with 500 employees, and several regional sales teams are located throughout their market base.Adventure Works sells products wholesale to specialty shops and to individuals through theInternet. For the data mining exercises, you will work with the AdventureWorksDW Internet sales tables, which contain realistic patterns that work well for data mining exercises.For more information on Adventure Works Cycles see "Sample Databases and Business Scenarios" in SQL Server Books Online.Database DetailsThe Internet sales schema contains information about 9,242 customers. These customers live in six countries, which are combined into three regions:North America (83%)Europe (12%)Australia (7%)The database contains data for three fiscal years: 2002, 2003, and 2004.The products in the database are broken down by subcategory, model, and product.Business Intelligence Development StudioBusiness Intelligence Development Studio is a set of tools designed for creating business intelligence projects. Because Business Intelligence Development Studio was created as an IDE environment in which you can create a complete solution, you work disconnected from the server. You can change your data mining objects as much as you want, but the changes are not reflected on the server until after you deploy the project.Working in an IDE is beneficial for the following reasons:The Analysis Services project is the entry point for a business intelligence solution. An Analysis Services project encapsulates mining models and OLAP cubes, along with supplemental objects that make up the Analysis Services database. From Business Intelligence Development Studio, you can create and edit Analysis Services objects within a project and deploy the project to the appropriate Analysis Services server or servers.If you are working with an existing Analysis Services project, you can also use Business Intelligence Development Studio to work connected the server. In this way, changes are reflected directly on the server without having to deploy the solution.SQL Server Management StudioSQL Server Management Studio is a collection of administrative and scripting tools for working with Microsoft SQL Server components. This workspace differs from Business Intelligence Development Studio in that you are working in a connected environment where actions are propagated to the server as soon as you save your work.After the data has been cleaned and prepared for data mining, most of the tasks associated with creating a data mining solution are performed within Business Intelligence Development Studio. Using the Business Intelligence Development Studio tools, you develop and test the datamining solution, using an iterative process to determine which models work best for a given situation. When the developer is satisfied with the solution, it is deployed to an Analysis Services server. From this point, the focus shifts from development to maintenance and use, and thus SQL Server Management Studio. Using SQL Server Management Studio, you can administer your database and perform some of the same functions as in Business Intelligence Development Studio, such as viewing, and creating predictions from mining models.Data Transformation ServicesData Transformation Services (DTS) comprises the Extract, Transform, and Load (ETL) tools in SQL Server 2005. These tools can be used to perform some of the most important tasks in data mining: cleaning and preparing the data for model creation. In data mining, you typically perform repetitive data transformations to clean the data before using the data to train a mining model. Using the tasks and transformations in DTS, you can combine data preparation and model creation into a single DTS package.DTS also provides DTS Designer to help you easily build and run packages containing all of the tasks and transformations. Using DTS Designer, you can deploy the packages to a server and run them on a regularly scheduled basis. This is useful if, for example, you collect data weekly data and want to perform the same cleaning transformations each time in an automated fashion.You can work with a Data Transformation project and an Analysis Services project together as part of a business intelligence solution, by adding each project to a solution in Business Intelligence Development Studio.Mining Model AlgorithmsData mining algorithms are the foundation from which mining models are created. The variety of algorithms included in SQL Server 2005 allows you to perform many types of analysis. For more specific information about the algorithms and how they can be adjusted using parameters, see "Data Mining Algorithms" in SQL Server Books Online.Microsoft Decision TreesThe Microsoft Decision Trees algorithm supports both classification and regression and it works well for predictive modeling. Using the algorithm, you can predict both discrete and continuous attributes.In building a model, the algorithm examines how each input attribute in the dataset affects the result of the predicted attribute, and then it uses the input attributes with the strongest relationship to create a series of splits, called nodes. As new nodes are added to the model, a tree structure begins to form. The top node of the tree describes the breakdown of the predicted attribute over the overall population. Each additional node is created based on the distribution of states of the predicted attribute as compared to the input attributes. If an input attribute is seen tocause the predicted attribute to favor one state over another, a new node is added to the model. The model continues to grow until none of the remaining attributes create a split that provides an improved prediction over the existing node. The model seeks to find a combination of attributes and their states that creates a disproportionate distribution of states in the predicted attribute, therefore allowing you to predict the outcome of the predicted attribute.Microsoft ClusteringThe Microsoft Clustering algorithm uses iterative techniques to group records from a dataset into clusters containing similar characteristics. Using these clusters, you can explore the data, learning more about the relationships that exist, which may not be easy to derive logically through casual observation. Additionally, you can create predictions from the clustering model created by the algorithm. For example, consider a group of people who live in the same neighborhood, drive the same kind of car, eat the same kind of food, and buy a similar version of a product. This is a cluster of data. Another cluster may include people who go to the same restaurants, have similar salaries, and vacation twice a year outside the country. Observing how these clusters are distributed, you can better understand how the records in a dataset interact, as well as how that interaction affects the outcome of a predicted attribute.Microsoft Naïve BayesThe Microsoft Naïve Bayes algorithm quickly builds mining models that can be used for classification and prediction. It calculates probabilities for each possible state of the input attribute, given each state of the predictable attribute, which can later be used to predict an outcome of the predicted attribute based on the known input attributes. The probabilities used to generate the model are calculated and stored during the processing of the cube. The algorithm supports only discrete or discretized attributes, and it considers all input attributes to be independent. The Microsoft Naïve Bayes algorithm produces a simple mining model that can be considered a starting point in the data mining process. Because most of the calculations used in creating the model are generated during cube processing, results are returned quickly. This makes the model a good option for exploring the data and for discovering how various input attributes are distributed in the different states of the predicted attribute.Microsoft Time SeriesThe Microsoft Time Series algorithm creates models that can be used to predict continuous variables over time from both OLAP and relational data sources. For example, you can use the Microsoft Time Series algorithm to predict sales and profits based on the historical data in a cube.Using the algorithm, you can choose one or more variables to predict, but they must be continuous. You can have only one case series for each model. The case series identifies the location in a series, such as the date when looking at sales over a length of several months or years.A case may contain a set of variables (for example, sales at different stores). The Microsoft Time Series algorithm can use cross-variable correlations in its predictions. For example, prior sales at one store may be useful in predicting current sales at another store.Microsoft Neural NetworkIn Microsoft SQL Server 2005 Analysis Services, the Microsoft Neural Network algorithm creates classification and regression mining models by constructing a multilayer perceptron network of neurons. Similar to the Microsoft Decision Trees algorithm provider, given each state of the predictable attribute, the algorithm calculates probabilities for each possible state of the input attribute. The algorithm provider processes the entire set of cases , iteratively comparing the predicted classification of the cases with the known actual classification of the cases. The errors from the initial classification of the first iteration of the entire set of cases is fed back into the network, and used to modify the network's performance for the next iteration, and so on. You can later use these probabilities to predict an outcome of the predicted attribute, based on the input attributes. One of the primary differences between this algorithm and the Microsoft Decision Trees algorithm, however, is that its learning process is to optimize network parameters toward minimizing the error while the Microsoft Decision Trees algorithm splits rules in order to maximize information gain. The algorithm supports the prediction of both discrete and continuous attributes.Microsoft Linear RegressionThe Microsoft Linear Regression algorithm is a particular configuration of the Microsoft Decision Trees algorithm, obtained by disabling splits (the whole regression formula is built in a single root node). The algorithm supports the prediction of continuous attributes.Microsoft Logistic RegressionThe Microsoft Logistic Regression algorithm is a particular configuration of the Microsoft Neural Network algorithm, obtained by eliminating the hidden layer. The algorithm supports the prediction of both discrete andcontinuous attributes.)中文译文数据挖掘技术简介摘要：微软® SQL Server™2005中提供用于创建和使用数据挖掘模型的集成环境的工作。
Data-mining-clustering数据挖掘—聚类分析大学毕业论文外文文献翻译及原文
Data-mining-clustering数据挖掘—聚类分析大学毕业论文外文文献翻译及原文毕业设计（论文）外文文献翻译文献、资料中文题目：聚类分析文献、资料英文题目：clustering文献、资料来源：文献、资料发表（出版）日期：院（部）：专业：自动化班级：姓名：学号：指导教师：翻译日期： 2017.02.14外文翻译英文名称：Data mining-clustering译文名称：数据挖掘—聚类分析专业：自动化姓名：****班级学号：****指导教师：******译文出处：Data mining：Ian H.Witten, EibeFrank 著Clustering5.1 INTRODUCTIONClustering is similar to classification in that data are grouped. However, unlike classification, the groups are not predefined. Instead, the grouping is accomplished by finding similarities between data according to characteristics found in the actualdata. The groups are called clusters. Some authors view clustering as a special type of classification. In this text, however, we follow a more conventional view in that the two are different. Many definitions for clusters have been proposed:●Set of like elements. Elements from different clusters are not alike.●The distance between points in a cluster is less than the distance betweena point in the cluster and any point outside it.A term similar to clustering is database segmentation, where like tuple (record) in a database are grouped together. This is done to partition or segment the database into components that then give the user a more general view of the data. In this case text, we do not differentiate between segmentation and clustering. A simple example of clustering is found in Example 5.1. This example illustrates the fact that that determining how to do the clustering is not straightforward.As illustrated in Figure 5.1, a given set of data may be clustered on different attributes. Here a group of homes in a geographic area is shown. The first floor type of clustering is based on the location of the home. Homes that are geographically close to each other are clustered together. In the second clustering, homes are grouped based on the size of the house.Clustering has been used in many application domains, including biology, medicine, anthropology, marketing, and economics. Clustering applications include plant and animal classification, disease classification, image processing, pattern recognition, and document retrieval. One of the first domains in which clustering was used was biological taxonomy. Recent usesinclude examining Web log data to detect usage patterns.When clustering is applied to a real-world database, many interesting problems occur:●Outlier handling is difficult. Here the elements do not naturally fallinto any cluster. They can be viewed as solitary clusters. However, if aclustering algorithm attempts to find larger clusters, these outliers will beforced to be placed in some cluster. This process may result in the creationof poor clusters by combining two existing clusters and leaving the outlier in its own cluster.● Dynamic data in the database implies that cluster membership may change over time.● Interpreting the semantic meaning of each cluster may be difficult. With classification, the labeling of the classes is known ahead of time. However, with clustering, this may not be the case. Thus, when the clustering process finishes creating a set of clusters, the exact meaning of each cluster may not be obvious. Here is where a domain expert is needed to assign a label or interpretation for each cluster.● There is no one correct answer to a clustering problem. In fact, many answers may be found. The exact number of clusters required is not easy to determine. Again, a domain expert may be required. For example, suppose we have a set of data about plants that have been collected during a field trip. Without any prior knowledge of plant classification, if we attempt to divide this set of data into similar groupings, it would not be clear how many groups should be created.● Another related iss ue is what data should be used of clustering. Unlike learning during a classification process, where there is some a priori knowledge concerning what the attributes of each classification should be, in clustering we have no supervised learning to aid the process. Indeed, clustering can be viewed as similar to unsupervised learning.We can then summarize some basic features of clustering (as opposed to classification):● The (best) number of clusters is not known.● There may not be any a priori knowledge co ncerning the clusters. ● Cluster results are dynamic.The clustering problem is stated as shown in Definition 5.1. Here we assume that the number of clusters to be created is an input value, k. The actual content (and interpretation) of each cluster,j k ,1j k ≤≤, is determined as a result of the function definition. Without loss of generality, we will view that the result of solving a clustering problem is that a set of clusters is created: K={12,,...,k k k k }.D EFINITION 5.1.Given a database D ={12,,...,n t t t } of tuples and aninteger value k , the clustering problem is to define a mapping f : {1,...,}D k → where each i t is assigned to one cluster j K ,1j k ≤≤. A cluster j K , contains precisely those tuples mapped to it; that is,j K ={|(),1,i i j t f t K i n =≤≤and i t D ∈}.A classification of the different types of clustering algorithms is shown in Figure 5.2. Clustering algorithms themselves may be viewed as hierarchical or partitional. With hierarchical clustering, a nested set of clusters is created. Each level in the hierarchy has a separate set of clusters. At the lowest level, each item is in itsown unique cluster. At the highest level, all items belong to the same cluster. With hierarchical clustering, the desired number of clusters is not input. With partitional clustering, the algorithm creates only one set of clusters. These approaches use the desired number of clusters to drive how the final set is created. Traditional clustering algorithms tend to be targeted to small numeric database that fit into memory .There are, however, more recent clustering algorithms that look at categorical data and are targeted to larger, perhaps dynamic, databases. Algorithms targeted to larger databases may adapt to memory constraints by either sampling the database or using data structures, which can be compressed or pruned to fit into memory regardless of the size of the database. Clustering algorithms may also differ based on whether they produce overlapping or nonoverlapping clusters. Even though we consider only nonoverlapping clusters, it is possible to place an item in multiple clusters. In turn, nonoverlapping clusters can be viewed as extrinsic or intrinsic. Extrinsic techniques use labeling of the items to assist in the classification process. These algorithms are the traditional classification supervised learning algorithms in which a special input training set is used. Intrinsic algorithms do not use any a priori category labels, but depend only on the adjacency matrix containing the distance between objects. All algorithms we examine in this chapter fall into the intrinsic class.The types of clustering algorithms can be furthered classified based on the implementation technique used. Hierarchical algorithms can becategorized as agglomerat ive or divisive. ”Agglomerative ” implies that the clusters are created in a bottom-up fashion, while divisive algorithms work in a top-down fashion. Although bothhierarchical and partitional algorithms could be described using the agglomerative vs. divisive label, it typically is more associated with hierarchical algorithms. Another descriptive tag indicates whether each individual element is handled one by one, serial (sometimes called incremental), or whether all items are examined together, simultaneous. If a specific tuple is viewed as having attribute values for all attributes in the schema, then clustering algorithms could differ as to how the attribute values are examined. As is usually done with decision tree classification techniques, some algorithms examine attribute values one at a time, monothetic. Polythetic algorithms consider all attribute values at one time. Finally, clustering algorithms can be labeled base on the mathematical formulation given to the algorithm: graph theoretic or matrix algebra. In this chapter we generally use the graph approach and describe the input to the clustering algorithm as an adjacency matrix labeled with distance measure.We discuss many clustering algorithms in the following sections. This is only a representative subset of the many algorithms that have been proposed in the literature. Before looking at these algorithms, we first examine possible similarity measures and examine the impact of outliers.5.2 SIMILARITY AND DISTANCE MEASURESThere are many desirable properties for the clusters created by a solution to a specific clustering problem. The most important one is that a tuple within one cluster is more like tuples within that cluster than it is similar to tuples outside it. As with classification, then, we assume the definition of a similarity measure, sim(,i l t t ), defined between any two tuples, ,i l t t D . This provides a more strict and alternative clustering definition, as found in Definition 5.2. Unless otherwise stated, we use thefirst definition rather than the second. Keep in mind that the similarity relationship stated within the second definition is a desirable, although not always obtainable, property.A distance measure, dis(,i j t t ), as opposed to similarity, is often used in。
数据采集系统中英文对照外文翻译文献
中英文对照外文翻译(文档含英文原文和中文翻译)Data Acquisition SystemsData acquisition systems are used to acquire process operating data and store it on，secondary storage devices for later analysis. Many or the data acquisition systems acquire this data at very high speeds and very little computer time is left to carry out any necessary, or desirable, data manipulations or reduction. All the data are stored on secondary storage devices and manipulated subsequently to derive the variables ofin-terest. It is very often necessary to design special purpose data acquisition systems and interfaces to acquire the high speed process data. This special purpose design can be an expensive proposition.Powerful mini- and mainframe computers are used to combine the data acquisition with other functions such as comparisons between the actual output and the desirable output values, and to then decide on the control action which must be taken to ensure that the output variables lie within preset limits. The computing power required will depend upon the type of process control system implemented. Software requirements for carrying out proportional, ratio or three term control of process variables are relatively trivial, and microcomputers can be used to implement such process control systems. It would not be possible to use many of the currently available microcomputers for the implementation of high speed adaptive control systems which require the use of suitable process models and considerable online manipulation of data.Microcomputer based data loggers are used to carry out intermediate functions such as data acquisition at comparatively low speeds, simple mathematical manipulations of raw data and some forms of data reduction. The first generation of data loggers, without any programmable computing facilities, was used simply for slow speed data acquisition from up to one hundred channels. All the acquired data could be punched out on paper tape or printed for subsequent analysis. Such hardwired data loggers are being replaced by the new generation of data loggers which incorporate microcomputers and can be programmed by the user. They offer an extremely good method of collecting the process data, using standardized interfaces, and subsequently performing the necessary manipulations to provide the information of interest to the process operator. The data acquired can be analyzed to establish correlations, if any, between process variables and to develop mathematical models necessary for adaptive and optimal process control.The data acquisition function carried out by data loggers varies from one to 9 in system to another. Simple data logging systems acquire data from a few channels while complex systems can receive data from hundreds, or even thousands, of input channels distributed around one or more processes. The rudimentary data loggers scan the selected number of channels, connected to sensors or transducers, in a sequential manner and the data are recorded in a digital format. A data logger can be dedicated in the sense that it can only collect data from particular types of sensors and transducers. It is best to use a nondedicated data logger since any transducer or sensor can be connected to the channels via suitable interface circuitry. This facility requires the use of appropriate signal conditioning modules.Microcomputer controlled data acquisition facilitates the scanning of a large number of sensors. The scanning rate depends upon the signal dynamics which means that some channels must be scanned at very high speeds in order to avoid aliasing errors while there is very little loss of information by scanning other channels at slower speeds. In some data logging applications the faster channels require sampling at speeds of up to 100 times per second while slow channels can be sampled once every five minutes. The conventional hardwired, non-programmable data loggers sample all the channels in a sequential manner and the sampling frequency of all the channels must be the same. This procedure results in the accumulation of very large amounts of data, some of which is unnecessary, and also slows down the overall effective sampling frequency. Microcomputer based data loggers can be used to scan some fast channels at a higher frequency than other slow speed channels.The vast majority of the user programmable data loggers can be used to scan up to 1000 analog and 1000 digital input channels. A small number of data loggers, with a higher degree of sophistication, are suitable for acquiring data from up to 15, 000 analog and digital channels. The data from digital channels can be in the form of Transistor- Transistor Logic or contact closure signals. Analog data must be converted into digital format before it is recorded and requires the use of suitable analog to digital converters (ADC)．The characteristics of the ADC will define the resolution that can be achieved and the rate at which the various channels can be sampled. An in-crease in the number of bits used in the ADC improves the resolution capability. Successive approximation ADC's arefaster than integrating ADC's. Many microcomputer controlled data loggers include a facility to program the channel scanning rates. Typical scanning rates vary from 2 channels per second to 10, 000 channels per second.Most data loggers have a resolution capability of ±0.01% or better, It is also pos-sible to achieve a resolution of 1 micro-volt. The resolution capability, in absolute terms, also depends upon the range of input signals, Standard input signal ranges are 0-10 volt, 0-50 volt and 0-100 volt. The lowest measurable signal varies form 1 t, volt to 50, volt. A higher degree of recording accuracy can be achieved by using modules which accept data in small, selectable ranges. An alternative is the auto ranging facil-ity available on some data loggers.The accuracy with which the data are acquired and logged-on the appropriate storage device is extremely important. It is therefore necessary that the data acquisi-tion module should be able to reject common mode noise and common mode voltage. Typical common mode noise rejection capabilities lie in the range 110 dB to 150 dB. A decibel (dB) is a tern which defines the ratio of the power levels of two signals. Thus if the reference and actual signals have power levels of N, and Na respectively, they will have a ratio of n decibels, wheren＝10 Log10（Na /Nr）Protection against maximum common mode voltages of 200 to 500 volt is available on typical microcomputer based data loggers.The voltage input to an individual data logger channel is measured, scaled and linearised before any further data manipulations or comparisons are carried out.In many situations, it becomes necessary to alter the frequency at which particu-lar channels are sampled depending upon the values of data signals received from a particular input sensor. Thus a channel might normally be sampled once every 10 minutes. If, however, the sensor signals approach the alarm limit, then it is obviously desirable to sample that channel once every minute or even faster so that the operators can be informed, thereby avoiding any catastrophes. Microcomputer controlledintel-ligent data loggers may be programmed to alter the sampling frequencies depending upon the values of process signals. Other data loggers include self-scanning modules which can initiate sampling.The conventional hardwired data loggers, without any programming facilities, simply record the instantaneous values of transducer outputs at a regular samplingin-terval. This raw data often means very little to the typical user. To be meaningful, this data must be linearised and scaled, using a calibration curve, in order to determine the real value of the variable in appropriate engineering units. Prior to the availability of programmable data loggers, this function was usually carried out in the off-line mode on a mini- or mainframe computer. The raw data values had to be punched out on pa-per tape, in binary or octal code, to be input subsequently to the computer used for analysis purposes and converted to the engineering units. Paper tape punches are slow speed mechanical devices which reduce the speed at which channels can be scanned. An alternative was to print out the raw data values which further reduced the data scanning rate. It was not possible to carry out any limit comparisons or provide any alarm information. Every single value acquired by the data logger had to be recorded eventhough it might not serve any useful purpose during subsequent analysis; many data values only need recording when they lie outside the pre-set low and high limits.If the analog data must be transmitted over any distance, differences in ground potential between the signal source and final location can add noise in the interface design. In order to separate common-mode interference form the signal to be recorded or processed, devices designed for this purpose, such as instrumentation amplifiers, may be used. An instrumentation amplifier is characterized by good common-mode- rejection capability, a high input impedance, low drift, adjustable gain, and greater cost than operational amplifiers. They range from monolithic ICs to potted modules, and larger rack-mounted modules with manual scaling and null adjustments. When a very high common-mode voltage is present or the need for extremely-lowcom-mon-mode leakage current exists（as in many medical-electronics applications），an isolation amplifier is required. Isolation amplifiers may use optical or transformer isolation.Analog function circuits are special-purpose circuits that are used for a variety of signal conditioning operations on signals which are in analog form. When their accu-racy is adequate, they can relieve the microprocessor of time-consuming software and computations. Among the typical operations performed are multiplications, division, powers, roots, nonlinear functions such as for linearizing transducers, rimsmeasure-ments, computing vector sums, integration and differentiation, andcurrent-to-voltage or voltage- to-current conversion. Many of these operations can be purchased in available devices as multiplier/dividers, log/antilog amplifiers, and others.When data from a number of independent signal sources must be processed by the same microcomputer or communications channel, a multiplexer is used to channel the input signals into the A/D converter.Multiplexers are also used in reverse, as when a converter must distribute analog information to many different channels. The multiplexer is fed by a D／A converter which continually refreshes the output channels with new information.In many systems, the analog signal varies during the time that the converter takes to digitize an input signal. The changes in this signal level during the conversion process can result in errors since the conversion period can be completed some time after the conversion command. The final value never represents the data at the instant when the conversion command is transmitted. Sample-hold circuits are used to make an acquisition of the varying analog signal and to hold this signal for the duration of the conversion process. Sample-hold circuits are common in multichannel distribution systems where they allow each channel to receive and hold the signal level.In order to get the data in digital form as rapidly and as accurately as possible, we must use an analog/digital (A/D) converter, which might be a shaft encoder, a small module with digital outputs, or a high-resolution, high-speed panel instrument. These devices, which range form IC chips to rack-mounted instruments, convert ana-log input data, usually voltage, into an equivalent digital form. The characteristics of A/D converters include absolute and relative accuracy, linearity, monotonic, resolu-tion, conversion speed, and stability. A choice of input ranges, output codes, and other features are available. The successive-approximation technique is popular for a large number ofapplications, with the most popular alternatives being the counter-comparator types, and dual-ramp approaches. The dual-ramp has been widely-used in digital voltmeters.D/A converters convert a digital format into an equivalent analog representation. The basic converter consists of a circuit of weighted resistance values or ratios, each controlled by a particular level or weight of digital input data, which develops the output voltage or current in accordance with the digital input code. A special class of D/A converter exists which have the capability of handling variable reference sources. These devices are the multiplying DACs. Their output value is the product of the number represented by the digital input code and the analog reference voltage, which may vary form full scale to zero, and in some cases, to negative values.Component Selection CriteriaIn the past decade, data-acquisition hardware has changed radically due to ad-vances in semiconductors, and prices have come down too; what have not changed, however, are the fundamental system problems confronting the designer. Signals may be obscured by noise, rfi，ground loops, power-line pickup, and transients coupled into signal lines from machinery. Separating the signals from these effects becomes a matter for concern.Data-acquisition systems may be separated into two basic categories:（1）those suited to favorable environments like laboratories -and（2）those required for hostile environments such as factories, vehicles, and military installations. The latter group includes industrial process control systems where temperature information may be gathered by sensors on tanks, boilers, wats, or pipelines that may be spread over miles of facilities. That data may then be sent to a central processor to provide real-time process control. The digital control of steel mills, automated chemical production, and machine tools is carried out in this kind of hostile environment. The vulnerability of the data signals leads to the requirement for isolation and other techniques.At the other end of the spectrum-laboratory applications, such as test systems for gathering information on gas chromatographs, mass spectrometers, and other sophis-ticated instruments-the designer's problems are concerned with the performing of sen-sitive measurements under favorable conditions rather than with the problem ofpro-tecting the integrity of collected data under hostile conditions.Systems in hostile environments might require components for wide tempera-tures, shielding, common-mode noise reduction, conversion at an early stage, redun-dant circuits for critical measurements, and preprocessing of the digital data to test its reliability. Laboratory systems, on the other hand, will have narrower temperature ranges and less ambient noise. But the higher accuracies require sensitive devices, and a major effort may be necessary for the required signal /noise ratios.The choice of configuration and components in data-acquisition design depends on consideration of a number of factors:1. Resolution and accuracy required in final format.2. Number of analog sensors to be monitored.3. Sampling rate desired.4. Signal-conditioning requirement due to environment and accuracy.5. Cost trade-offs.Some of the choices for a basic data-acquisition configuration include：1 ．Single-channel techniques.A. Direct conversion.B. Preamplification and direct conversion.C. Sample-hold and conversion.D. Preamplification, sample-hold, and conversion.E. Preamplification, signal-conditioning, and direct conversion.F. Preamplification, signal-conditioning, sample-hold, and conversion.2. Multichannel techniques.A. Multiplexing the outputs of single-channel converters.B. Multiplexing the outputs of sample-holds.C. Multiplexing the inputs of sample-holds.D. Multiplexing low-level data.E. More than one tier of multiplexers.Signal-conditioning may include:1. Radiometric conversion techniques.B. Range biasing.D. Logarithmic compression.A. Analog filtering.B. Integrating converters.C. Digital data processing.We shall consider these techniques later, but first we will examine some of the components used in these data-acquisition system configurations.MultiplexersWhen more than one channel requires analog-to-digital conversion, it is neces-sary to use time-division multiplexing in order to connect the analog inputs to a single converter, or to provide a converter for each input and then combine the converter outputs by digital multiplexing.Analog MultiplexersAnalog multiplexer circuits allow the timesharing of analog-to-digital converters between a numbers of analog information channels. An analog multiplexer consists of a group of switches arranged with inputs connected to the individual analog channels and outputs connected in common（as shown in Fig. 1）．The switches may be ad-dressed by a digital input code.Many alternative analog switches are available in electromechanical and solid-state forms. Electromechanical switch types include relays, stepper switches,cross-bar switches, mercury-wetted switches, and dry-reed relay switches. The best switching speed is provided by reed relays（about 1 ms）．The mechanical switches provide high do isolation resistance, low contact resistance, and the capacity to handle voltages up to 1 KV, and they are usually inexpensive. Multiplexers using mechanical switches are suited to low-speed applications as well as those having high resolution requirements. They interface well with the slower A/D converters, like the integrating dual-slope types. Mechanical switches have a finite life, however, usually expressed innumber of operations. A reed relay might have a life of 109 operations, which wouldallow a 3-year life at 10 operations/second.Solid-state switch devices are capable of operation at 30 ns, and they have a life which exceeds most equipment requirements. Field-effect transistors（FETs）are used in most multiplexers. They have superseded bipolar transistors which can introduce large voltage offsets when used as switches.FET devices have a leakage from drain to source in the off state and a leakage from gate or substrate to drain and source in both the on and off states. Gate leakage in MOS devices is small compared to other sources of leakage. When the device has a Zener-diode-protected gate, an additional leakage path exists between the gate and source.Enhancement-mode MOS-FETs have the advantage that the switch turns off when power is removed from the MUX. Junction-FET multiplexers always turn on with the power off.A more recent development, the CMOS-complementary MOS-switch has the advantage of being able to multiplex voltages up to and including the supply voltages. A±10-V signal can be handled with a ±10-V supply.Trade-off Considerations for the DesignerAnalog multiplexing has been the favored technique for achieving lowest system cost. The decreasing cost of A/D converters and the availability of low-cost, digital integrated circuits specifically designed for multiplexing provide an alternative with advantages for some applications. A decision on the technique to use for a givensys-tem will hinge on trade-offs between the following factors:1. Resolution. The cost of A/D converters rises steeply as the resolution increases due to the cost of precision elements. At the 8-bit level, the per-channel cost of an analog multiplexer may be a considerable proportion of the cost of a converter. At resolutions above 12 bits, the reverse is true, and analog multiplexing tends to be more economical.2. Number of channels. This controls the size of the multiplexer required and the amount of wiring and interconnections. Digital multiplexing onto a common data bus reduces wiring to a minimum in many cases. Analog multiplexing is suited for 8 to 256 channels; beyond this number, the technique is unwieldy and analog errors be-come difficult to minimize. Analog and digital multiplexing is often combined in very large systems.3. Speed of measurement, or throughput. High-speed A/D converters can add a considerable cost to the system. If analog multiplexing demands a high-speedcon-verter to achieve the desired sample rate, a slower converter for each channel with digital multiplexing can be less costly．4. Signal level and conditioning. Wide dynamic ranges between channels can be difficult with analog multiplexing. Signals less than 1V generally require differential low-level analog multiplexing which is expensive, with programmable-gain amplifiers after the MUX operation. The alternative of fixed-gain converters on each channel, with signal-conditioning designed for the channel requirement, with digital multi-plexing may be more efficient.5. Physical location of measurement points. Analog multiplexing is suitedfor making measurements at distances up to a few hundred feet from the converter, since analog lines may suffer from losses, transmission-line reflections, and interference. Lines may range from twisted wire pairs to multiconductor shielded cable, depending on signal levels, distance, and noise environments. Digital multiplexing is operable to thousands of miles, with the proper transmission equipment, for digital transmission systems can offer the powerful noise-rejection characteristics that are required for29 Data Acquisition Systems long-distance transmission.Digital MultiplexingFor systems with small numbers of channels, medium-scale integrated digital multiplexers are available in TTL and MOS logic families. The 74151 is a typical example. Eight of these integrated circuits can be used to multiplex eight A/D con-verters of 8-bit resolution onto a common data bus.This digital multiplexing example offers little advantages in wiring economy, but it is lowest in cost, and the high switching speed allows operation at sampling rates much faster than analog multiplexers. The A/D converters are required only to keep up with the channel sample rate, and not with the commutating rate. When large numbers of A/D converters are multiplexed, the data-bus technique reduces system interconnections. This alone may in many cases justify multiple A/D converters. Data can be bussed onto the lines in bit-parallel or bit-serial format, as many converters have both serial and parallel outputs. A variety of devices can be used to drive the bus, from open collector and tristate TTL gates to line drivers and optoelectronic isolators. Channel-selection decoders can be built from 1-of-16 decoders to the required size. This technique also allows additional reliability in that a failure of one A/D does not affect the other channels. An important requirement is that the multiplexer operate without introducing unacceptable errors at the sample-rate speed. For a digital MUX system, one can determine the speed from propagation delays and the time required to charge the bus capacitance.Analog multiplexers can be more difficult to characterize. Their speed is a func-tion not only of internal parameters but also external parameters such as channel, source impedance, stray capacitance and the number of channels, and the circuit lay-out. The user must be aware of the limiting parameters in the system to judge their ef-fect on performance.The nonideal transmission and open-circuit characteristics of analog multiplexers can introduce static and dynamic errors into the signal path. These errors include leakage through switches, coupling of control signals into the analog path, and inter-actions with sources and following amplifiers. Moreover, the circuit layout can com-pound these effects.Since analog multiplexers may be connected directly to sources which may have little overload capacity or poor settling after overloads, the switches should have a break-before-make action to prevent the possibility of shorting channels together. It may be necessary to avoid shorted channels when power is removed and a chan-nels-off with power-down characteristic is desirable. In addition to the chan-nel-addressing lines, which are normally binary-coded, it is useful to have inhibited or enable lines to turn all switches off regardless of the channel being addressed. This simplifies the external logic necessary to cascade multiplexers and can also be useful in certain modes of channeladdressing. Another requirement for both analog and digital multiplexers is the tolerance of line transients and overload conditions, and the ability to absorb the transient energy and recover without damage.数据采集系统数据采集系统是用来获取数据处理和存储在二级存储设备，为后来的分析。
数据挖掘论文英文版
Jilin Province’s population growth and energy consumption analysisMajor StatisticsStudent No. 0401083710Name Niu FukuanJilin Province’s population growth andenergy consumption analysis[Summary]Since the third technological revolution, the energy has become the lifeline of national economy, while the energy on Earth is limited, so in between the major powers led to a number of oil-related or simply a war for oil. In order to compete on the world's resources and energy control, led to the outbreak of two world wars. China's current consumption period coincided with the advent of high-energy, CNPC, Sinopec, CNOOC three state-owned oil giants have been "going out" to develop international markets, Jilin Province as China's energy output and energy consumption province, is also active in the energy corresponding diplomacy. Economic globalization and increasingly fierce competition in the energy environment, China's energy policy is still there are many imperfections, to a certain extent, affect the energy and population development of Jilin Province, China and even to some extent can be said existing population crisis is the energy crisis.[Keyword]Energy consumption; Population; Growth; Analysis;Data sourceI select data from "China Statistical Yearbook 2009" Jilin Province 1995-2007 comprehensive annual financial data (Table 1). Record of the total population (end) of the annual data sequence {Xt}, mind full of energy consumption (kg of standard coal) annual data sequence {Yt}.Table 1 1995-2007 older and province GDP per capita consumption level of all data2001 127627 16629798.1 11.75686723 16.626706712002 128453 17585215.7 11.76331836 16.682569092003 129227 19888035.3 11.76932583 16.805628872004 129988 21344029.6 11.77519742 16.876282612005 130756 23523004.4 11.78108827 16.973489412006 131448 25592925.6 11.78636662 17.057826532007 132129 26861825.7 11.791534 17.106216721.Timing diagramFirst, the total population of Table 1 (end) of the annual data series {Xt}, full of energy consumption (kg of standard coal) annual data series {Yt} are drawn timing diagram, in order to observe the annual population data series {Xt} and national annual energy consumption data sequence {Yt} is stationary, by EVIEWS software output is shown below.Figure 1 of the total population (end) sequence timing diagramFigure 2 universal life energy consumption (kg of standard coal) sequence timing diagramFigure 1 is a sequence {Xt} the timing diagram, Figure 2 is a sequence {Yt} of the timing diagram.Two figures show both the total population (end) or universal life energy consumption (kg of standard coal) index showed a rising trend, the total population of the annual data series {Xt} and national annual energy consumption data sequence {Yt} not smooth, the two may have long-term cointegration relationship.2. Data smoothing(1)Sequence LogarithmFigures 1 and 2 by the intuitive discovery data sequence {Xt} and {Yt} showed a significant growth trend, a significant non-stationary sequence. Therefore, the total population of first sequence {Xt} and universal life energy consumption (kg of standard coal) {Yt}, respectively for the number of treatment to eliminate heteroscedasticity. That logx = lnXt, logy = lnYt, with a view to the target sequence into the linear trend trend sequence, by EVIEWS software operations, the number of sequence timing diagram, in which the population sequence {logx} timing diagram shown in Figure 3, the full sequence of energy consumption {logy} timing diagram shown in Figure 4.Figure3 Figure 4Figure 3 shows the total population observed sequence {logx} and universal life energy consumption (kg of standard coal) sequence {logy} index trend has been basically eliminated, the two have obvious long-term cointegration relationship, which is the transfer function modeling an important prerequisite. However, the above sequence of numbers is still non-stationary series. Respectively {logx} and {logy} sequence of ADF unit root test (Table 5 and Table 6), the test results as shown below. (2)Unit root testHere we will be on the province's total population and the whole sequence {Xt} energy consumption (kg of standard coal) sequence data {Yt} be the unit root test, the results obtained by Eviews software operation is as follows:Table 2 Of the total population sequence {logx}Obtained from Table 2: Total population sequence data {Xt} of the ADF is -0.784587, significantly larger than the 1% level in the critical test value of -4.3260, the 5% level greater than the critical value of -3.2195 testing, but also greater than 10% level in the critical test value -2.7557, so the total population of the data sequence {logx} {Xt} is a non-stationary series.Table 3 National energy consumption (kg of standard coal) unit root test {logy}Obtained from Table 3: National energy consumption (kg of standard coal) data {Yt} of the ADF is 0.489677, significantly larger than the 1% level in the critical test value of -4.3260, the 5% level greater than the critical test value of -3.2195, but also 10% greater than the critical level test value -2.7557, so the total population of the sequence {logx} data {Yt} is a non-stationary series.(3) Sequence of differentialBecause of the number of time series after still not a smooth sequence, so the need for further logarithm of the total population after the sequence {logx} and after a few of the universal life energy consumption (kg of standard coal) differential sequence data {logY} differential sequences were recorded as {▽logx} and {▽logy}. Are respectively the second-order differential of the total population of the sequence {▽logX} and second-order differential of the national energy consumption (kg of standard coal) sequence data {▽ logy} the ADF unit root test (Table 7 and Table 8), test results the following table.Table 4Table 4 shows that the total population of second-order differential sequence {▽logx} ADF value is -10.6278, apparently less than 1% level in the critical test value of -6.292057, less than the 5% level in the critical test value -4.450425 also 10% less than the level in the critical test value of -3.701534, second-order differential of the total population of the sequence {▽ logx} is a stationary sequence.Table5 5Table 5 shows that the second-order differential universal life energy consumption (kg of standard coal) {▽logy} of the ADF is -6.395029, apparently less than 1% level in the critical test value of -4.4613, less than the 5 % level of the critical test value of -3.2695, but also less than the 10% level the critical value of -2.7822 testing,universal life, second-order differential consumption of energy (kg of standard coal) {▽ logy} is a stationary sequence.3. Cointegration(1)Cointegration regressionCointegration theory in the 1980s there Engle Granger put forward specific, it is from the analysis of non-stationary time series start to explore the non-stationary variable contains the long-run equilibrium relationship between the non-stationary time series modeling provides a new solution.As the population time series {Xt} and universal life energy consumption time series {Yt} are logarithmic, the total population obtained by the analysis of time series {logX} and universal life energy consumption time series {logY} are second-order single whole sequence, so they may exist cointegration relationship. The results obtained by Eviews software operation is as follows:Table 6Obtained from Table 6:D(LNE2)= -0.054819 – 101.8623D(LOGX2)t = (-1.069855) (-1.120827)R2=0.122487 DW=1.593055（2）Check the smoothness of the residual sequenceFrom the Eviews software, get residual sequence analysis:Table 7Residual series unit root testObtained from Table 7: second-order differential value of -5.977460 ADF residuals, significantly less than 1% level in the critical test value -4.6405, less than 5% level in the critical test value of -3.3350, but also less than 10% level in the critical test value of -2.8169. Therefore, the second-order difference of the residual et is a stationary time series sequence. Expressed as follows:D（ET,2）=-0.042260-1.707007D(ET(-1),2)t = （-0.783744）(-5.977460)DW= 1.603022 EG=-5.977460，Since EG =- 5.977460, check the AFG cointegration test critical value table (N = 2, = 0.05, T = 16) received, EG value is less than the critical value, so to accept the original sequence et is stationary assumption. So you can determine the total population and energy consumption of all the people living there are two variables are long-term cointegration relationship.4. ECM model to establishThrough the above analysis, after the second-order differential of the logarithm of the total population time series {▽ logX} and second-order differential of Logarithm of of national energy consumption time series {▽ logY} is a stationary sequence, the second-order differential residuals et is also a stationary series. So that the number of second-order differential of the national energy consumption time series {▽ logY} as the dependent variable, after the second-order differential of the logarithm of the total population time series {▽logX} and second-order differential as residuals et from variable regression estimation, using Eviews software, the following findings:Table 8ECM model resultsTable 8 can be written by the ECM standard regression model, results are as follows:D(logY2)= -0.047266-154.4568D(LNP2) +0.171676D(ET2)t = (-1.469685) (-2.528562) (1.755694)R2= 0.579628 DW=1.760658ECM regression equation of the regression coefficients by a significance test, the error correction coefficient is positive, in line with forward correction mechanism. The estimation results show that the province of everyone's life changes in energy consumption depends not only on the change of the total population, but also on the previous year's total population deviation from the equilibrium level. In addition, the regression results show that short-term changes in the total population of all the people living there is a positive impact on energy consumption. Because short-term adjustment coefficient is significant, it shows that all the people living in JilinProvince annual consumption of energy in its long-run equilibrium value is the deviation can be corrected well.5. ARMA model(1) Model to identifyAfter differential differenced stationary series into stationary time series, after the analysis can be used ARMR model, the choice of using the model of everyone's life before the first stable after the annual energy consumption time series {logY} to estimate the first full life energy consumption sequence {logY} do autocorrelation and partial autocorrelation, the results of the following:Table 9{logy} of the autocorrelation and partial autocorrelation mapObtained from Table 9, the relevant figure from behind, after K = 1 in a random interval, partial autocorrelation can be seen in K = 1 after a random interval. So we can live on national energy consumption to establish the sequence {logY} ARMA (1,1) model, following on the ARMA (1,1) model parameter estimation, which results in the following table:Table 10ARMA (1,1) model parameter estimationTable 10 obtained by the ARMA (1,1) model parameter estimation is given by: D(LNE,2)=0.014184+0.008803D(LNE,2)t-1-0.858461U t-1(2)ARMA (1,2) model testModel of the residuals obtained for white noise test, if the residuals are not white noise sequence, then the need for ARMA (1,2) model for further improvement; if it is white noise process, the acceptance of the original model. ARMA (1,2) model residuals test results are as follows:Table11 ARMA (1,2) model residuals testTable 11 shows, Q statistic P value greater than 0.05, so the ARMA (1,1) model, the residual series is white noise sequence and accept the ARMA (1,1) model. Our whole life to predict changes in energy consumption, the results are as follows:Figure 5 National energy consumption forecast mapJilin Province of everyone's life through the forecast energy consumption, we can see all the people living consumption of energy is rising every year, which also shows that in the future for many years, Jilin Province, universal life energy consumption will be showing an upward trend. And because of the total population and the existence of universal life energy consumption effects of changes in the same direction, so the total population over the next many years, will continue to increase.6. ProblemsBased on the province's total population and the national energy consumption cointegration analysis of the relationship between population and energy consumption obtained between Jilin Province, there are long-term stability of the interaction and mutual promotion of the long-run equilibrium relationship. The above analysis can be more accurate understanding of the energy consumption of Jilin Province, Jilin Province put forward a better proposal on energy conservation. Moment, Jilin Province facing energy problems:(1) The heavy industry still accounts for a large proportion of;(2)The scale of energy-intensive industry, the rapid growth of production ofenergy saving effect;(3)The coal-based energy consumption is still.7.Recommendation:(1) Population control, and actively cooperate with the national policy of family planning, ease the pressure on the average population can consume.(2) Raise awareness of the importance of energy saving, the implementation of energy-saving target responsibility system, energy efficiency are implemented.Conscientiously implement the State Council issued the statistics of energy saving, monitoring and evaluation program of the three systems. Strict accountability.(3) Speed up industrial restructuring and transformation of economic development. Speed up industrial restructuring and transformation of economic development, to overcome the resource, energy and other bottlenecks, and take the high technological content, good economic returns, low resources consumption, little environmental pollution and human resources into full play to the new industrialization path.(4) Should pay attention to quality improvement and optimization of the structure, so that the final implementation of the restructuring to improve the overall quality of industrial and economic growth, quality and efficiency up.(5) To enhance the development and promotion of energy-saving technologies, strengthen energy security, promotion of renewable energy, clean energy.Adhere to technical progress and the deepening of reform and opening up the combination. To enhance the independent innovation capability as the adjustment of industrial structure, changing the growth mode of the central link, speed up the innovation system, efforts to address the constraints of the city development major science and technology. Vigorously promote the recycling economy demonstration pilot enterprises to actively carry out comprehensive utilization of resources and renewable resources recycling. And actively promote solar, wind, biogas, biodiesel and other renewable energy construction.References[1] Wang Yan, Applied time series analysis of the Chinese People's University Press, 2008.12[2] Pang Hao. Econometric Science Press, 2006.1。
数据挖掘技术综述毕业论文外文翻译
Summary of Data Mining TechnologyAbstract: With the development of computer and network technology, it is very easy to obtain relevant information. But for the large number of large-scale data, the traditional statistical methods can not complete the analysis of such data. Therefore, an intelligent, comprehensive application of a variety of statistical analysis, database, intelligent language to analyze large data data "data mining" (Date Mining) technology came into being. This paper mainly introduces the basic concept of data mining and the method of data mining. The application of data mining and its development prospect are also described in this paper.Keywords: data mining; method; application; foreground1 IntroductionWith the rapid development of information technology, the scale of the database has been expanding, resulting in a lot of data. The surge of data is hidden behind a lot of important information, people want to be able to conduct a higher level of analysis in order to make better use of these data. In order to provide decision makers with a unified global perspective, data warehouses are established in many areas. But a lot of data often makes it impossible to identify hidden in which can provide support for decision-making information, and the traditional query, reporting tools can not meet the needs of mining this information. Therefore, the need for a new data analysis technology to deal with large amounts of data, and from the extraction of valuable potential knowledge, data mining (Data Mining) technology came into being. Data mining technology is also accompanied by the development of data warehouse technology and gradually improved.2 Data Mining Technology2.1 Definition of data miningData mining refers to the non-trivial process of automatically extracting useful information hidden in the data from the data set. The information is represented by rules, concepts, rules and patterns. It helps decision makers analyze historical data and current data and discover hidden relationships and patterns to predict future behaviors that may occur. The process of data mining is also called the process of knowledge discovery. It is a kind of interdisciplinary and interdisciplinary subject, which involves the fields of database, artificial intelligence, mathematical statistics, visualization and parallel computing. Data mining is a new information processing technology, its main feature is the database of large amounts of data extraction, conversion, analysis and other modelprocessing, and extract the auxiliary decision-making key data. Data mining is an important technology in KDD (Knowledge Discovery in Database). It does not use the standard database query language (such as SQL) to query, but the content of the query to summarize the pattern and the inherent law of the search. Traditional query and report processing are only the result of the incident, and there is no in-depth study of the reasons for the occurrence of data mining is the main understanding of the causes of occurrence, and with a certain degree of confidence in the future forecast for the decision-making behavior to provide favorable stand by.2.2 Methods of data miningData mining research combines a number of different disciplines in the field of technology and results, making the current data mining methods show a variety of forms. From the perspective of statistical analysis, the data mining models used in statistical analysis techniques are linear and non-linear analysis, regression analysis, logistic regression analysis, univariate analysis, multivariate analysis, time series analysis, recent sequence analysis, and recent Oracle algorithm and clustering analysis and other methods. Using these techniques, you can examine the data in those unusual forms, and then interpret the data using various statistical models and mathematical models to explain the market rules and business opportunities that are hidden behind those data. Knowledge discovery class Data mining technology is a kind of mining technology which is completely different from the statistical analysis class data mining technology, including artificial neural network, support vector machine, decision tree, genetic algorithm, rough set, rule discovery and association order.2.2.1 Statistical methodsTraditional statistics provide a number of discriminant and regression analysis methods for data mining. Commonly used techniques such as Bayesian reasoning, regression analysis, and variance analysis. Bayesian reasoning is the basic principle of correcting the probability distribution of data sets after knowing new information Tools, to deal with the classification of data mining problems, regression analysis used to find an input variable and the relationship between the output variables of the best model, in the regression analysis used to describe a variable trends and other variables of the relationship between the linear regression, There is also a logarithmic regression for predicting the occurrence of certain events. The variance analysis in the statistical method is generally used to analyze the effects of estimating the regression line's performance and the independent variables on the final regression, which is the result of many mining applications One of the powerful tools.2.2.2 Association rulesThe association rule is a simple and practical analysis rule, which describes the law and pattern of some attributes in one thing at the same time, which is one of the most mature and important technologies in data mining. It is made by R. Agrawal et al. First proposed that the most classical association rule mining algorithm is Apriori, which first digs out all frequent itemsets, and then generates association rules from frequent itemsets. Many mining rules of frequent rule sets are It evolved from the evolution of the rules in the field of data mining is widely used in large data sets to find a meaningful relationship between the data, one of the reasons is that it is not only a choice of a dependent variable, the association rules in the data The most typical application of the mining area is the shopping basket analysis. Most association rule mining algorithms can discover all the associated relationships hidden in the mining data, and the amount of association rules is often very large. However, not all the relationships between the attributes obtained through the association are practical. Value, the effective evaluation of these association rules, screening out the user is really interested, meaningful association rules is particularly important.2.2.3 Clustering analysisCluster analysis is based on the criteria associated with the selected samples to be divided into several groups, the same group of samples with high similarity, different groups are different, commonly used techniques have split algorithm, cohesion algorithm, Clustering and incremental clustering. The clustering method is suitable for the internal relationship between the samples, so as to make a reasonable evaluation of the sample structure. In addition, the cluster analysis is also used to detect the isolated points. Sometimes clustering is not intended to get objects together but to make it easier for an object to be separated from other objects. Cluster analysis has been applied to a variety of areas such as economic analysis, pattern recognition, image processing, and especially in business. Clustering analysis can help marketers discover different groups of characteristics that exist in customer groups. The key to clustering analysis In addition to the choice of algorithms, it is the choice of metrics for the sample. The classes that are not derived from the clustering algorithm are effective for decision making. Before applying an algorithm, the clustering trend of the data is usually checked first.2.2.4 Decision tree methodDecision tree learning is a method of approximating discrete objective functions by classifying instances from a root node to a leaf node to classify an instance. The leaf node is the classification of the instance. Each node on the tree illustrates a test of anattribute of the instance, and each subsequent branch of the node corresponds to a possible value of the attribute. The method of sorting the instance is from the root node of the tree, Test the properties specified by this node, and then move down the corresponding branch of the attribute value for the given instance. Decision tree method is to be applied to the classification of data mining.2.2.5 neural networkThe neural network is based on the mathematical model of self-learning, which can analyze a large number of complex data and can complete the extremely complex pattern extraction and trend analysis for human brain or other computer. The neural network can be expressed as guidance The learning can also be a non-guided cluster, whichever is the value entered into the neural network. Artificial neural network is used to simulate the structure of human brain neurons. Based on MP model and Hebb learning rules, three kinds of neural networks are established, which have non-linear mapping characteristics, information storage, parallel processing and global collective action, High degree of self-learning, self-organizing and adaptive ability. The feedforward neural network is represented by the sensor network and BP network, which can be used for classification and prediction. The feedback network is represented by Hopfield network for associative memory and optimization. The self-organizing network is based on ART model, Kohonon The model is represented for clustering.2.2.6 support vector machineSupport vector machine (SVM) is a new machine learning method developed on the basis of statistical learning theory. It is based on the principle of structural risk minimization, as far as possible to improve the learning machine generalization ability, has good promotion performance and good classification accuracy, can effectively solve the learning problem, has become a training multi-layer sensor, RBF An Alternative Method for Neural Networks and Polynomial Neural Networks. In addition, the support vector machine algorithm is a convex optimization problem, the local optimal solution must be the global optimal solution, these features are including the neural network, including other algorithms can not and. Support vector machine can be applied to the classification of data mining, regression, the exploration of unknown things and so on. In addition to the above methods, there are ways to convert data and results into visualization techniques, cloud model methods, and inductive logic programs.In fact, any kind of excavation tool is often based on specific issues to select the appropriate mining method, it is difficult to say which method is good, that method is inferior, but depending on the specific problems.2.3 data mining processFor data mining, we can be divided into three main stages: data preparation, data mining, evaluation and expression of results. The results of the evaluation and expression can also be broken down into: assessment, interpretation model model, consolidation, the use of knowledge. Knowledge discovery in the database is a multi-step process, but also the three stages of the repeated process,2.3.1 Data PreparationKDD processing object is a lot of data, these data are generally stored in the database system, the long-term accumulation of the results. But often not suitable for direct knowledge mining on these data, need to do data preparation, generally including the choice of data (select the relevant data), clean (eliminate noise, data), speculate (estimate missing data), conversion (discrete Data conversion between data and continuous value data, packet classification of data values, calculation combinations between data items, etc.), data reduction (reduction of data volume). These jobs are often prepared when the data warehouse is generated. Data preparation is the first step in KDD. Whether data preparation is good will affect the efficiency and accuracy of data mining and the effectiveness of the final model.2.3.2 Data miningData mining is the most critical step KDD, but also technical difficulties. Most of the research KDD personnel are studying data mining technology, using more technology to have decision tree, classification, clustering, rough set, association rules, neural network, genetic algorithm and so on. Data mining According to the goal of KDD, select the parameters of the corresponding algorithm, analyze the data, and get the model model of the possible model layer knowledge.2.3.3 Results evaluation and expressionEvaluation model: the model model obtained above, there may be no practical significance or no use value, it may not be able to accurately reflect the true meaning of the data, even in some cases is contrary to the facts, so need Evaluate, determine which are valid and useful patterns. Evaluation can be based on years of experience, some models can also be used directly to test the accuracy of the data. This step also includes presenting the pattern to the user in an easy-to-understand manner.Consolidate knowledge: the user understands and is considered to be consistent with the actual and valuable model of the model that forms the knowledge. But also pay attention to the consistency of knowledge to check, with the knowledge obtained before the conflict, contradictory embankment, so that knowledge is consolidated.The use of knowledge: to find knowledge is to use, how to make knowledge can be used is one of the steps of KDD. There are two ways to use knowledge: one is to rely on the relationship or result described by the knowledge itself to support decision-making; the other is to require the use of new data knowledge, which may produce new problems, and Need to further optimize the knowledge. The process of KDD may need to be repeated multiple times. Once each step does not match the expected target, go back to the previous step, re-adjust, and re-execute.3 data mining applicationsThe potential application of data mining is very broad: government management decision-making, business management, scientific research and industrial enterprise decision support and other fields.3.1 Applied in scientific researchFrom the point of view of scientific research methodology, scientific research can be divided into three categories: theoretical science, experimental science and computational science. Computational science is an important symbol of modern science. Computing scientists work with data and analyze a wide variety of experimental or observational data every day. With the use of advanced scientific data collection tools, such as observing satellites, remote sensors, DNA molecular technology, the amount of data is very large, the traditional data analysis tools can not do anything, so there must be a strong intelligent automatic data analysis tools Caixing. Data mining in astronomy has a very famous application system: SKICAT (Sky Image Cataloging andAnalysis Tool). It is a tool developed by the California Institute of Technology's Jet Propulsion Laboratory (a laboratory designed to design a Mars probe rover) and astronomical scientists to help astronomers discover distant quasars. SKICAT is both the first successful data mining application and one of the first successful applications of artificial intelligence in astronomy and space science. Using SKICAT, astronomers have discovered 16 new and distant quasars that help astronomers better study the formation of quasars and the structure of the early universe. The application of data mining in biology is mainly focused on the study of molecular biology, especially genetic engineering. Gene research, there is a well-known international research project - the human genome project.3.2 in the commercial applicationIn the business sector, especially in the retail industry, the use of data mining is more successful. As the MIS system in the commercial use of universal, especially the use of code technology, you can collect a lot of data on the purchase situation, and the amount of data in the surge. The use of data mining technology can provide managers with theright decision-making means, so to promote sales and improve competitiveness is of great help.3.3 in the financial applicationIn the financial sector, the amount of data is very large, banks, securities companies and other transaction data and storage capacity is great. And for credit card fraud, the bank's annual loss is very large. Therefore, you can use data mining to analyze the customer's reputation. Typical financial analysis areas include investment assessment and stock trading market forecasts.3.4 in medical applicationsData mining in the medical application is very wide, from molecular medicine to medical diagnosis, can use data mining means to improve efficiency and efficiency. In the case of drug synthesis, the analysis of the chemical structure of the drug molecule can determine which of the atoms or atomic genes in the drug can play a role in the disease, so that in the synthesis of new drugs, according to the molecular structure of the drug to determine the drug will be possible What kind of disease? Data mining can also be used in industry, agriculture, transportation, telecommunications, military, Internet and other industries. Data mining has a wide range of application prospects, it can be applied to decision support, can also be applied to the database management system (DBMS). Data mining as a tool for decision support and analysis can be used to construct a knowledge base. In DBMS, data mining can be used for semantic query optimization, integrity constraints and inconsistent checks.4 Development Trend of Data MiningDue to the diversity of data, data mining tasks and data mining methods, many challenging topics are proposed for data mining. At the same time, the design of data mining language, efficient and useful data mining methods and system development, interactive and integrated data mining environment, as well as the application of data mining technology to solve large application problems, are currently data mining researchers, systems And the main problems faced by application developers. At present, the development trend of data mining is mainly as follows: application exploration; scalable data mining method; data mining and database system, data warehouse system and Web database system integration; data mining language standardization; visual data mining; Complex mining of new data types; Web mining; data mining in the privacy protection and information security.5 concluding remarksAt present, although the data mining technology has been applied to a certain degree, andachieved remarkable results, but there are still many unresolved problems, such as data preprocessing, mining algorithms, pattern recognition and interpretation, visualization problems. For the business process, the most critical issue of data mining is how to combine the spatial and temporal characteristics of business data, will be excavated out of knowledge, that is, time and space knowledge expression and interpretation mechanism. With the deepening of data mining technology, data mining technology will be applied in a wider range of areas, and achieved more significant results.Reference[1] HAN Jia-wei,KAMBER M. Data Mining Concepts and Technigues [M]. FAN Ming,MENG Xiao-feng,trrnsl. Beijing:China Ma-chine Press,2010. 305-307.(in Chinese)[2] ZHOU Bin,LIU Ya-ping,WU Ouan-yuan. The design and implementations issues of a data mining systems for eIectronic commerce[J]. Computer Engineering,2012,26 (6) :18-20.(in Chinese)[3] WANG Jia-cai,CHEN Oi,ZHAO Jie-yu,etla. VISMiner:An interactive visua I data mining prototyped system [J] . Computer Engi-neering,2003,29 (1) :17-19.(in Chinese)[4] LIU Kan,ZHOU Xiao-zheng,ZHOU Dong-ru. Visua I data mining based on para IIe I coordinates [J]. Computer Engineering and Ap-p Iications,2013,39 (5) : 193-196.(in Chinese)[5] NETZA,CHAUDHURI S,FAYYAD U,et al. Integrating data mining with SOL databases:OLE DB for data mining [A] . Pro 17th Int Conf on Data Engineering [C]. Heide Iberg:IEEE,2001. 379-387.[6] ZHAO Zhi-hong,LUO Bin,CHEN Shi-fu. A structure of data mining system based on data warehouse [J] . Computer App Iications and Software,2012,19 (4) :27-30.(in Chinese)[7] OIAN Wei-ning,WEI Li,WANG Yan,et a I. A data mining system for very Iarge databases [J]. Journa I of Software, 2012, 13 (8) :1540-1545.(in Chinese)[8] Quanyin Zhu，Jin Ding，Yonghua Yin，et al. A HybridApproach for New Products Discovery of Cell PhoneBased on Web Mining[J]. Journal of Information andComputational Science. 2012，9( 16) : 5039－5046.[9]Quanyin Zhu，Pei Zhou，Sunqun Cao，et al. A novel RDB－SW approach for commodities price dynamic trend a-nalysis based on Web extracting[J]. Journal of Digital In-formation Management，2012，10( 4) : 230－235.[10]Quanyin Zhu，Pei Zhou. The System Architecture for theBasic Information of Science and Technology ExpertsBased on Distributed Storage and Web Mining[C]. Pro-ceedings of the International Conference on ComputerScience and Service System，2012: 661－664.数据挖掘技术综述摘要：随着计算机、网络技术的发展，获得有关资料非常简单易行。
大数据挖掘外文翻译文献
文献信息：文献标题：A Study of Data Mining with Big Data（大数据挖掘研究）国外作者：VH Shastri，V Sreeprada文献出处：《International Journal of Emerging Trends and Technology in Computer Science》,2016,38(2):99-103字数统计：英文2291单词，12196字符；中文3868汉字外文文献：A Study of Data Mining with Big DataAbstract Data has become an important part of every economy, industry, organization, business, function and individual. Big Data is a term used to identify large data sets typically whose size is larger than the typical data base. Big data introduces unique computational and statistical challenges. Big Data are at present expanding in most of the domains of engineering and science. Data mining helps to extract useful data from the huge data sets due to its volume, variability and velocity. This article presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective.Keywords: Big Data, Data Mining, HACE theorem, structured and unstructured.I.IntroductionBig Data refers to enormous amount of structured data and unstructured data thatoverflow the organization. If this data is properly used, it can lead to meaningful information. Big data includes a large number of data which requires a lot of processing in real time. It provides a room to discover new values, to understand in-depth knowledge from hidden values and provide a space to manage the data effectively. A database is an organized collection of logically related data which can be easily managed, updated and accessed. Data mining is a process discovering interesting knowledge such as associations, patterns, changes, anomalies and significant structures from large amount of data stored in the databases or other repositories.Big Data includes 3 V’s as its characteristics. They are volume, velocity and variety. V olume means the amount of data generated every second. The data is in state of rest. It is also known for its scale characteristics. Velocity is the speed with which the data is generated. It should have high speed data. The data generated from social media is an example. Variety means different types of data can be taken such as audio, video or documents. It can be numerals, images, time series, arrays etc.Data Mining analyses the data from different perspectives and summarizing it into useful information that can be used for business solutions and predicting the future trends. Data mining (DM), also called Knowledge Discovery in Databases (KDD) or Knowledge Discovery and Data Mining, is the process of searching large volumes of data automatically for patterns such as association rules. It applies many computational techniques from statistics, information retrieval, machine learning and pattern recognition. Data mining extract only required patterns from the database in a short time span. Based on the type of patterns to be mined, data mining tasks can be classified into summarization, classification, clustering, association and trends analysis.Big Data is expanding in all domains including science and engineering fields including physical, biological and biomedical sciences.II.BIG DATA with DATA MININGGenerally big data refers to a collection of large volumes of data and these data are generated from various sources like internet, social-media, business organization, sensors etc. We can extract some useful information with the help of Data Mining. It is a technique for discovering patterns as well as descriptive, understandable, models from a large scale of data.V olume is the size of the data which is larger than petabytes and terabytes. The scale and rise of size makes it difficult to store and analyse using traditional tools. Big Data should be used to mine large amounts of data within the predefined period of time. Traditional database systems were designed to address small amounts of data which were structured and consistent, whereas Big Data includes wide variety of data such as geospatial data, audio, video, unstructured text and so on.Big Data mining refers to the activity of going through big data sets to look for relevant information. To process large volumes of data from different sources quickly, Hadoop is used. Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. Its distributed supports fast data transfer rates among nodes and allows the system to continue operating uninterrupted at times of node failure. It runs Map Reduce for distributed data processing and is works with structured and unstructured data.III.BIG DATA characteristics- HACE THEOREM.We have large volume of heterogeneous data. There exists a complex relationship among the data. We need to discover useful information from this voluminous data.Let us imagine a scenario in which the blind people are asked to draw elephant. The information collected by each blind people may think the trunk as wall, leg as tree, body as wall and tail as rope. The blind men can exchange information with each other.Figure1: Blind men and the giant elephantSome of the characteristics that include are:i.Vast data with heterogeneous and diverse sources: One of the fundamental characteristics of big data is the large volume of data represented by heterogeneous and diverse dimensions. For example in the biomedical world, a single human being is represented as name, age, gender, family history etc., For X-ray and CT scan images and videos are used. Heterogeneity refers to the different types of representations of same individual and diverse refers to the variety of features to represent single information.ii.Autonomous with distributed and de-centralized control: the sources are autonomous, i.e., automatically generated; it generates information without any centralized control. We can compare it with World Wide Web (WWW) where each server provides a certain amount of information without depending on other servers.plex and evolving relationships: As the size of the data becomes infinitely large, the relationship that exists is also large. In early stages, when data is small, there is no complexity in relationships among the data. Data generated from social media and other sources have complex relationships.IV.TOOLS:OPEN SOURCE REVOLUTIONLarge companies such as Facebook, Yahoo, Twitter, LinkedIn benefit and contribute work on open source projects. In Big Data Mining, there are many open source initiatives. The most popular of them are:Apache Mahout:Scalable machine learning and data mining open source software based mainly in Hadoop. It has implementations of a wide range of machine learning and data mining algorithms: clustering, classification, collaborative filtering and frequent patternmining.R: open source programming language and software environment designed for statistical computing and visualization. R was designed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand beginning in 1993 and is used for statistical analysis of very large data sets.MOA: Stream data mining open source software to perform data mining in real time. It has implementations of classification, regression; clustering and frequent item set mining and frequent graph mining. It started as a project of the Machine Learning group of University of Waikato, New Zealand, famous for the WEKA software. The streams framework provides an environment for defining and running stream processes using simple XML based definitions and is able to use MOA, Android and Storm.SAMOA: It is a new upcoming software project for distributed stream mining that will combine S4 and Storm with MOA.Vow pal Wabbit: open source project started at Yahoo! Research and continuing at Microsoft Research to design a fast, scalable, useful learning algorithm. VW is able to learn from terafeature datasets. It can exceed the throughput of any single machine networkinterface when doing linear learning, via parallel learning.V.DATA MINING for BIG DATAData mining is the process by which data is analysed coming from different sources discovers useful information. Data Mining contains several algorithms which fall into 4 categories. They are:1.Association Rule2.Clustering3.Classification4.RegressionAssociation is used to search relationship between variables. It is applied in searching for frequently visited items. In short it establishes relationship among objects. Clustering discovers groups and structures in the data.Classification deals with associating an unknown structure to a known structure. Regression finds a function to model the data.The different data mining algorithms are:Table 1. Classification of AlgorithmsData Mining algorithms can be converted into big map reduce algorithm based on parallel computing basis.Table 2. Differences between Data Mining and Big DataVI.Challenges in BIG DATAMeeting the challenges with BIG Data is difficult. The volume is increasing every day. The velocity is increasing by the internet connected devices. The variety is also expanding and the organizations’ capability to capture and process the data is limited.The following are the challenges in area of Big Data when it is handled:1.Data capture and storage2.Data transmission3.Data curation4.Data analysis5.Data visualizationAccording to, challenges of big data mining are divided into 3 tiers.The first tier is the setup of data mining algorithms. The second tier includesrmation sharing and Data Privacy.2.Domain and Application Knowledge.The third one includes local learning and model fusion for multiple information sources.3.Mining from sparse, uncertain and incomplete data.4.Mining complex and dynamic data.Figure 2: Phases of Big Data ChallengesGenerally mining of data from different data sources is tedious as size of data is larger. Big data is stored at different places and collecting those data will be a tedious task and applying basic data mining algorithms will be an obstacle for it. Next we need to consider the privacy of data. The third case is mining algorithms. When we are applying data mining algorithms to these subsets of data the result may not be that much accurate.VII.Forecast of the futureThere are some challenges that researchers and practitioners will have to deal during the next years:Analytics Architecture:It is not clear yet how an optimal architecture of analytics systems should be to deal with historic data and with real-time data at the same time. An interesting proposal is the Lambda architecture of Nathan Marz. The Lambda Architecture solves the problem of computing arbitrary functions on arbitrary data in real time by decomposing the problem into three layers: the batch layer, theserving layer, and the speed layer. It combines in the same system Hadoop for the batch layer, and Storm for the speed layer. The properties of the system are: robust and fault tolerant, scalable, general, and extensible, allows ad hoc queries, minimal maintenance, and debuggable.Statistical significance: It is important to achieve significant statistical results, and not be fooled by randomness. As Efron explains in his book about Large Scale Inference, it is easy to go wrong with huge data sets and thousands of questions to answer at once.Distributed mining: Many data mining techniques are not trivial to paralyze. To have distributed versions of some methods, a lot of research is needed with practical and theoretical analysis to provide new methods.Time evolving data: Data may be evolving over time, so it is important that the Big Data mining techniques should be able to adapt and in some cases to detect change first. For example, the data stream mining field has very powerful techniques for this task.Compression: Dealing with Big Data, the quantity of space needed to store it is very relevant. There are two main approaches: compression where we don’t loose anything, or sampling where we choose what is thedata that is more representative. Using compression, we may take more time and less space, so we can consider it as a transformation from time to space. Using sampling, we are loosing information, but the gains inspace may be in orders of magnitude. For example Feldman et al use core sets to reduce the complexity of Big Data problems. Core sets are small sets that provably approximate the original data for a given problem. Using merge- reduce the small sets can then be used for solving hard machine learning problems in parallel.Visualization: A main task of Big Data analysis is how to visualize the results. As the data is so big, it is very difficult to find user-friendly visualizations. New techniques, and frameworks to tell and show stories will be needed, as for examplethe photographs, infographics and essays in the beautiful book ”The Human Face of Big Data”.Hidden Big Data: Large quantities of useful data are getting lost since new data is largely untagged and unstructured data. The 2012 IDC studyon Big Data explains that in 2012, 23% (643 exabytes) of the digital universe would be useful for Big Data if tagged and analyzed. However, currently only 3% of the potentially useful data is tagged, and even less is analyzed.VIII.CONCLUSIONThe amounts of data is growing exponentially due to social networking sites, search and retrieval engines, media sharing sites, stock trading sites, news sources and so on. Big Data is becoming the new area for scientific data research and for business applications.Data mining techniques can be applied on big data to acquire some useful information from large datasets. They can be used together to acquire some useful picture from the data.Big Data analysis tools like Map Reduce over Hadoop and HDFS helps organization.中文译文：大数据挖掘研究摘要数据已经成为各个经济、行业、组织、企业、职能和个人的重要组成部分。
数据挖掘外文文献翻译中英文
数据挖掘外文文献翻译(含：英文原文及中文译文)英文原文What is Data Mining?Simply stated, data mining refers to extracting or “mining” knowledge from large amounts of data. The term is actually a misnomer. Remember that the mining of gold from rocks or sand is referred to as gold mining rather than rock or sand mining. Thus, “data mining” should have been more appropriately named “knowledge mining from data”, which is unfortunately somewhat long. “Knowledge mining”, a shorter term, may not reflect the emphasis on mining from large amounts of data. Nevertheless, mining is a vivid term characterizing the process that finds a small set of precious nuggets from a great deal of raw material. Thus, such a misnomer which carries both “data” and “mining” became a popular choice . There are many other terms carrying a similar or slightly different meaning to data mining, such as knowledge mining from databases, knowledge extraction, data / pattern analysis, data archaeology, and data dredging.Many people treat data mining as a synonym for another popularly used term, “Knowledge Discovery in Databases”, or KDD. Alternatively, others view data mining as simply an essential step in the process ofknowledge discovery in databases. Knowledge discovery consists of an iterative sequence of the following steps:· data cleaning: to remove noise or irrelevant data, · data integration: where multiple data sources may be combined,·data selection : where data relevant to the analysis task are retrieved from the database,· data transformati on : where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance,·data mining: an essential process where intelligent methods are applied in order to extract data patterns, · pattern evaluation: to identify the truly interesting patterns representing knowledge based on some interestingness measures, and·knowledge presentation: where visualization and knowledge representation techniques are used to present the mined knowledge to the user .The data mining step may interact with the user or a knowledge base. The interesting patterns are presented to the user, and may be stored as new knowledge in the knowledge base. Note that according to this view, data mining is only one step in the entire process, albeit an essential one since it uncovers hidden patterns for evaluation.We agree that data mining is a knowledge discovery process.However, in industry, in media, and in the database research milieu, the term “data mining” is becoming mo re popular than the longer term of “knowledge discovery in databases”. Therefore, in this book, we choose to use the term “data mining”. We adopt a broad view of data mining functionality: data mining is the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories.Based on this view, the architecture of a typical data mining system may have the following major components:1. Database, data warehouse, or other information repository. This is one or a set of databases, data warehouses, spread sheets, or other kinds of information repositories. Data cleaning and data integration techniques may be performed on the data.2. Database or data warehouse server. The database or data warehouse server is responsible for fetching the relevant data, based on the user’s data mining request.3. Knowledge base. This is the domain knowledge that is used to guide the search, or evaluate the interestingness of resulting patterns. Such knowledge can include concept hierarchies, used to organize attributes or attribute values into different levels of abstraction. Knowledge such as user beliefs, which can be used to assess a pattern’s interestingness based on its unexpectedness, may also be included. Otherexamples of domain knowledge are additional interestingness constraints or thresholds, and metadata (e.g., describing data from multiple heterogeneous sources).4. Data mining engine. This is essential to the data mining system and ideally consists of a set of functional modules for tasks such as characterization, association analysis, classification, evolution and deviation analysis.5. Pattern evaluation module. This component typically employs interestingness measures and interacts with the data mining modules so as to focus the search towards interesting patterns. It may access interestingness thresholds stored in the knowledge base. Alternatively, the pattern evaluation module may be integrated with the mining module, depending on the implementation of the data mining method used. For efficient data mining, it is highly recommended to push the evaluation of pattern interestingness as deep as possible into the mining process so as to confine the search to only the interesting patterns.6. Graphical user interface. This module communicates between users and the data mining system, allowing the user to interact with the system by specifying a data mining query or task, providing information to help focus the search, and performing exploratory data mining based on the intermediate data mining results. In addition, this component allows the user to browse database and data warehouse schemas or datastructures, evaluate mined patterns, and visualize the patterns in different forms.From a data warehouse perspective, data mining can be viewed as an advanced stage of on-1ine analytical processing (OLAP). However, data mining goes far beyond the narrow scope of summarization-style analytical processing of data warehouse systems by incorporating more advanced techniques for data understanding.While there may be many “data mining systems” on the market, not all of them can perform true data mining. A data analysis system that does not handle large amounts of data can at most be categorized as a machine learning system, a statistical data analysis tool, or an experimental system prototype. A system that can only perform data or information retrieval, including finding aggregate values, or that performs deductive query answering in large databases should be more appropriately categorized as either a database system, an information retrieval system, or a deductive database system.Data mining involves an integration of techniques from mult1ple disciplines such as database technology, statistics, machine learning, high performance computing, pattern recognition, neural networks, data visualization, information retrieval, image and signal processing, and spatial data analysis. We adopt a database perspective in our presentation of data mining in this book. That is, emphasis is placed on efficient andscalable data mining techniques for large databases. By performing data mining, interesting knowledge, regularities, or high-level information can be extracted from databases and viewed or browsed from different angles. The discovered knowledge can be applied to decision making, process control, information management, query processing, and so on. Therefore, data mining is considered as one of the most important frontiers in database systems and one of the most promising, new database applications in the information industry.A classification of data mining systemsData mining is an interdisciplinary field, the confluence of a set of disciplines, including database systems, statistics, machine learning, visualization, and information science. Moreover, depending on the data mining approach used, techniques from other disciplines may be applied, such as neural networks, fuzzy and or rough set theory, knowledge representation, inductive logic programming, or high performance computing. Depending on the kinds of data to be mined or on the given data mining application, the data mining system may also integrate techniques from spatial data analysis, Information retrieval, pattern recognition, image analysis, signal processing, computer graphics, Web technology, economics, or psychology.Because of the diversity of disciplines contributing to data mining, data mining research is expected to generate a large variety of datamining systems. Therefore, it is necessary to provide a clear classification of data mining systems. Such a classification may help potential users distinguish data mining systems and identify those that best match their needs. Data mining systems can be categorized according to various criteria, as follows.1) Classification according to the kinds of databases mined. A data mining system can be classified according to the kinds of databases mined. Database systems themselves can be classified according to different criteria (such as data models, or the types of data or applications involved), each of which may require its own data mining technique. Data mining systems can therefore be classified accordingly.For instance, if classifying according to data models, we may have a relational, transactional, object-oriented, object-relational, or data warehouse mining system. If classifying according to the special types of data handled, we may have a spatial, time -series, text, or multimedia data mining system , or a World-Wide Web mining system . Other system types include heterogeneous data mining systems, and legacy data mining systems.2) Classification according to the kinds of knowledge mined. Data mining systems can be categorized according to the kinds of knowledge they mine, i.e., based on data mining functionalities, such as characterization, discrimination, association, classification, clustering,trend and evolution analysis, deviation analysis , similarity analysis, etc.A comprehensive data mining system usually provides multiple and/or integrated data mining functionalities.Moreover, data mining systems can also be distinguished based on the granularity or levels of abstraction of the knowledge mined, including generalized knowledge(at a high level of abstraction), primitive-level knowledge(at a raw data level), or knowledge at multiple levels (considering several levels of abstraction). An advanced data mining system should facilitate the discovery of knowledge at multiple levels of abstraction.3) Classification according to the kinds of techniques utilized.Data mining systems can also be categorized according to the underlying data mining techniques employed. These techniques can be described according to the degree of user interaction involved (e.g., autonomous systems, interactive exploratory systems, query-driven systems), or the methods of data analysis employed(e.g., database-oriented or data warehouse-oriented techniques, machine learning, statistics, visualization, pattern recognition, neural networks, and so on ) .A sophisticated data mining system will often adopt multiple data mining techniques or work out an effective, integrated technique which combines the merits of a few individual approaches.中文译文什么是数据挖掘?简而言之，数据挖掘是指从大量数据中提取或“挖掘”知识。
数据挖掘技术英语
数据挖掘技术英语English:Data mining techniques refer to a variety of methods and algorithms used to extract useful patterns, trends, and insights from large datasets. These techniques encompass a wide range of approaches, including clustering, classification, regression, association rule mining, and anomaly detection, among others. Clustering involves grouping similar data points together based on their characteristics or attributes, enabling the identification of inherent structures within the data. Classification assigns predefined categories or labels to instances based on their features, allowing for the prediction of the class of new, unseen data points. Regression aims to establish relationships between variables by predicting continuous numerical outcomes, which is useful for forecasting and trend analysis. Association rule mining discovers interesting relationships or associations among variables in large datasets, commonly used in market basket analysis and recommendation systems. Anomaly detection identifies data points that deviate significantly from the norm, aiding in fraud detection, network security, and quality control. These data mining techniques are crucial in various industries such asfinance, healthcare, retail, and telecommunications, empowering organizations to make data-driven decisions, enhance operational efficiency, and gain a competitive edge.中文翻译:数据挖掘技术指的是从大型数据集中提取有用模式、趋势和洞见的各种方法和算法。
大数据外文翻译参考文献综述
大数据外文翻译参考文献综述(文档含中英文对照即英文原文和中文翻译)原文：Data Mining and Data PublishingData mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the partyrunning the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model [1] which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.Although data mining is potentially useful, many data holders are reluctant to provide their data for data mining for the fear of violating individual privacy. In recent years, study has been made to ensure that the sensitive information of individuals cannot be identified easily.Anonymity Models, k-anonymization techniques have been the focus of intense research in the last few years. In order to ensure anonymization of data while at the same time minimizing the informationloss resulting from data modifications, everal extending models are proposed, which are discussed as follows.1.k-Anonymityk-anonymity is one of the most classic models, which technique that prevents joining attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. In the k-anonymous tables, a data set is k-anonymous (k ≥ 1) if each record in the data set is in- distinguishable from at least (k . 1) other records within the same data set. The larger the value of k, the better the privacy is protected. k-anonymity can ensure that individuals cannot be uniquely identified by linking attacks.2. Extending ModelsSince k-anonymity does not provide sufficient protection against attribute disclosure. The notion of l-diversity attempts to solve this problem by requiring that each equivalence class has at least l well-represented value for each sensitive attribute. The technology of l-diversity has some advantages than k-anonymity. Because k-anonymity dataset permits strong attacks due to lack of diversity in the sensitive attributes. In this model, an equivalence class is said to have l-diversity if there are at least l well-represented value for the sensitive attribute. Because there are semantic relationships among the attribute values, and different values have very different levels of sensitivity. Afteranonymization, in any equivalence class, the frequency (in fraction) of a sensitive value is no more than α.3. Related Research AreasSeveral polls show that the public has an in- creased sense of privacy loss. Since data mining is often a key component of information systems, homeland security systems, and monitoring and surveillance systems, it gives a wrong impression that data mining is a technique for privacy intrusion. This lack of trust has become an obstacle to the benefit of the technology. For example, the potentially beneficial data mining re- search project, Terrorism Information Awareness (TIA), was terminated by the US Congress due to its controversial procedures of collecting, sharing, and analyzing the trails left by individuals. Motivated by the privacy concerns on data mining tools, a research area called privacy-reserving data mining (PPDM) emerged in 2000. The initial idea of PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. The solutions were often tightly coupled with the data mining algorithms under consideration. In contrast, privacy-preserving data publishing (PPDP) may not necessarily tie to a specific data mining task, and the data mining task is sometimes unknown at the time of data publishing. Furthermore, some PPDP solutions emphasize preserving the datatruthfulness at the record level, but PPDM solutions often do not preserve such property. PPDP Differs from PPDM in Several Major Ways as Follows ：1) PPDP focuses on techniques for publishing data, not techniques for data mining. In fact, it is expected that standard data mining techniques are applied on the published data. In contrast, the data holder in PPDM needs to randomize the data in such a way that data mining results can be recovered from the randomized data. To do so, the data holder must understand the data mining tasks and algorithms involved. This level of involvement is not expected of the data holder in PPDP who usually is not an expert in data mining.2) Both randomization and encryption do not preserve the truthfulness of values at the record level; therefore, the released data are basically meaningless to the recipients. In such a case, the data holder in PPDM may consider releasing the data mining results rather than the scrambled data.3) PPDP primarily “anonymizes” the data by hiding the identity of record owners, whereas PPDM seeks to directly hide the sensitive data. Excellent surveys and books in randomization and cryptographic techniques for PPDM can be found in the existing literature. A family of research work called privacy-preserving distributed data mining (PPDDM) aims at performing some data mining task on a set of private databasesowned by different parties. It follows the principle of Secure Multiparty Computation (SMC), and prohibits any data sharing other than the final data mining result. Clifton et al. present a suite of SMC operations, like secure sum, secure set union, secure size of set intersection, and scalar product, that are useful for many data mining tasks. In contrast, PPDP does not perform the actual data mining task, but concerns with how to publish the data so that the anonymous data are useful for data mining. We can say that PPDP protects privacy at the data level while PPDDM protects privacy at the process level. They address different privacy models and data mining scenarios. In the field of statistical disclosure control (SDC), the research works focus on privacy-preserving publishing methods for statistical tables. SDC focuses on three types of disclosures, namely identity disclosure, attribute disclosure, and inferential disclosure. Identity disclosure occurs if an adversary can identify a respondent from the published data. Revealing that an individual is a respondent of a data collection may or may not violate confidentiality requirements. Attribute disclosure occurs when confidential information about a respondent is revealed and can be attributed to the respondent. Attribute disclosure is the primary concern of most statistical agencies in deciding whether to publish tabular data. Inferential disclosure occurs when individual information can be inferred with high confidence from statistical information of the published data.Some other works of SDC focus on the study of the non-interactive query model, in which the data recipients can submit one query to the system. This type of non-interactive query model may not fully address the information needs of data recipients because, in some cases, it is very difficult for a data recipient to accurately construct a query for a data mining task in one shot. Consequently, there are a series of studies on the interactive query model, in which the data recipients, including adversaries, can submit a sequence of queries based on previously received query results. The database server is responsible to keep track of all queries of each user and determine whether or not the currently received query has violated the privacy requirement with respect to all previous queries. One limitation of any interactive privacy-preserving query system is that it can only answer a sublinear number of queries in total; otherwise, an adversary (or a group of corrupted data recipients) will be able to reconstruct all but 1 . o(1) fraction of the original data, which is a very strong violation of privacy. When the maximum number of queries is reached, the query service must be closed to avoid privacy leak. In the case of the non-interactive query model, the adversary can issue only one query and, therefore, the non-interactive query model cannot achieve the same degree of privacy defined by Introduction the interactive model. One may consider that privacy-reserving data publishing is a special case of the non-interactivequery model.This paper presents a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explains their effects on Data Privacy. k-anonymity is used for security of respondents identity and decreases linking attack in the case of homogeneity attack a simple k-anonymity model fails and we need a concept which prevent from this attack solution is l-diversity. All tuples are arranged in well represented form and adversary will divert to l places or on l sensitive attributes. l-diversity limits in case of background knowledge attack because no one predicts knowledge level of an adversary. It is observe that using generalization and suppression we also apply these techniques on those attributes which doesn’t need th is extent of privacy and this leads to reduce the precision of publishing table. e-NSTAM (extended Sensitive Tuples Anonymity Method) is applied on sensitive tuples only and reduces information loss, this method also fails in the case of multiple sensitive tuples.Generalization with suppression is also the causes of data lose because suppression emphasize on not releasing values which are not suited for k factor. Future works in this front can include defining a new privacy measure along with l-diversity for multiple sensitive attribute and we will focus to generalize attributes without suppression using other techniques which are used to achieve k-anonymity because suppression leads to reduce the precision ofpublishing table.译文：数据挖掘和数据发布数据挖掘中提取出大量有趣的模式从大量的数据或知识。
大数据挖掘外文翻译文献
大数据挖掘外文翻译文献大数据挖掘是一种通过分析和解释大规模数据集来发现实用信息和模式的过程。
它涉及到从结构化和非结构化数据中提取知识和洞察力，以支持决策制定和业务发展。
随着互联网的迅猛发展和技术的进步，大数据挖掘已经成为许多领域的关键技术，包括商业、医疗、金融和社交媒体等。
在大数据挖掘中，外文翻译文献起着重要的作用。
外文翻译文献可以提供最新的研究成果和技术发展，匡助我们了解和应用最先进的大数据挖掘算法和方法。
本文将介绍一篇与大数据挖掘相关的外文翻译文献，以匡助读者深入了解这一领域的最新发展。
标题："A Survey of Big Data Mining Techniques for Knowledge Discovery"这篇文献是由Xiaojuan Zhu等人于2022年发表在《Expert Systems with Applications》杂志上的一篇综述文章。
该文献对大数据挖掘技术在知识发现方面的应用进行了全面的调研和总结。
以下是该文献的主要内容和贡献：1. 引言本文首先介绍了大数据挖掘的背景和意义。
随着互联网和传感器技术的快速发展，我们每天都会产生大量的数据。
这些数据包含了珍贵的信息和洞察力，可以用于改进业务决策和发现新的商机。
然而，由于数据量庞大和复杂性高，传统的数据挖掘技术已经无法处理这些数据。
因此，大数据挖掘成为了一种重要的技术。
2. 大数据挖掘的挑战本文接着介绍了大数据挖掘面临的挑战。
由于数据量庞大，传统的数据挖掘算法无法有效处理大规模数据。
此外，大数据通常是非结构化的，包含各种类型的数据，如文本、图象和视频等。
因此，如何有效地从这些非结构化数据中提取实用的信息和模式也是一个挑战。
3. 大数据挖掘技术接下来，本文介绍了一些常用的大数据挖掘技术。
这些技术包括数据预处理、特征选择、分类和聚类等。
数据预处理是指对原始数据进行清洗和转换，以提高数据质量和可用性。
特征选择是指从大量的特征中选择最实用的特征，以减少数据维度和提高模型性能。
客户关系管理中的数据挖掘技术【外文翻译】
客户关系管理中的数据挖掘技术【外文翻译】外文原文Data mining techniques for customerrelationship managementChris Rygielski , Jyun-Cheng Wang , David C. YenAbstractAdvancements in technology have made relationship marketing areality in recent years. Technologies such as data warehousing, data mining, and campaign management software have made customer relationship management a new area where firms can gain a competitive advantage. Particularly through data mining—the extraction of hidden predictive information fromlarge databases—organizations can identify valuable customers, predict future behaviors, and enable firms to make proactive, knowledge-driven decisions. The automated, future-oriented analyses made possible by data mining move beyond the analyses of past events typically provided by history-oriented tools such as decision support systems. Data mining tools answer business questions that in the past were too time-consuming to pursue. Yet, it is the answers to these questions make customer relationship management possible. Various techniques exist among data mining software, each with their own advantages and challenges for different types of applications. A particular dichotomy exists between neural networks and chi-square automated interaction detection (CHAID). While differing approaches abound in the realm ofdata mining, the use of some type of data mining is necessary to accomplish the goals of 'today’s customer relationship management philosophy.2002 Elsevier Science Ltd. All rights reserved.Keywords: Customer relationship management (CRM); Relationship marketing; Data mining; Neural networks;Chi-square automated interaction detection (CHAID); Privacy rights 1. IntroductionA new business culture is developing today. Within it, the economics of customer relationships are changing in fundamental ways, and companies are facing the need to implement new solutions and strategies that address these changes. The concepts of mass production and mass marketing,first created during the Industrial Revolution, are being supplanted by new ideas in which customer relationships are the central business issue. Firms today are concerned with increasing customer value through analysis of the customer lifecycle. The tools and technologies of data warehousing, data mining, and other customer relationship management (CRM) techniques afford new opportunities for businesses to act on the concepts of relationship marketing.The old model of ―design-build-sell‖ (a product-oriented view) is being replaced by ―sell-build-redesign‖ (acustomer-oriented view). The traditional process of massmarketing is being challenged by the new approach of one-to-one marketing. In the traditional process, the marketing goal is to reach more customers andexpand the customer base. But given the high cost of acquiring new customers, it makes better sense to conduct business with current customers. In so doing, the marketing focus shifts away from the breadth of customer base to the depth of each customer’s needs.The performance metric changes from market share to so-called ―wallet share‖. Businesses do not justdeal with customers in order to make transactions; they turn the opportunity to sell products into a service experience and endeavor to establish a long-term relationship with each customer. The advent of the Internet has undoubtedly contributed to the shift of marketing focus. As on-line information becomes more accessible and abundant, consumers become more informed and sophisticated. They are aware of all that is being offered, and they demand the best. To cope with this condition, businesses have to distinguish their products or services in a way that avoids the undesired result of becoming mere commodities. One effective way to distinguish themselves is with systems that can interactprecisely and consistently with customers. Collecting customer demographics and behavior data makes precision targeting possible. This kind of targeting also helps when devising an effective promotion plan to meet tough competition or identifying prospective customers when new products appear. Interacting with customers consistently means businesses must store transaction records and responses in an online system that is available to knowledgeable staff members who know how tointeract with it. The importance of establishing close customer relationships is recognized, and CRM is called for.It may seem that CRM is applicable only for managing relationships between businesses and consumers. A closer examination reveals that it is even more crucial for business customers. In business-to-business(B2B) environments, a tremendous amount of information is exchanged on a regular basis. For example, transactions are more numerous, custom contracts are more diverse,and pricing schemes are more complicated. CRM helps smooth the process when various representatives of seller and buyer companies communicate and collaborate. Customized catalogues,personalized business portals, and targeted product offers can simplify the procurement process and improve efficiencies for both companies. E-mail alerts and new product information tailored to different roles in the buyer company can help increase the effectiveness of the sales pitch. Trust and authority are enhanced if targeted academic reports or industry news are delivered to the relevant individuals. All of these can be considered among the benefits of CRM.Cap Gemini conducted a study to gauge company awareness and preparation of a CRM strategy [1]. Of the firms surveyed, 65% were aware of CRM technology and methods; 28% had CRM projects under study or in the implementation phase; 12% were in the operational phase. In 45% of the companies surveyed, implementation and monitoring of the CRM project had been initiated and controlled by top management. Thus, it isapparent that this is a new and emerging concept that is seen as a key strategic initiative.This article examines the concepts of customer relationship management and one of its components, data mining. It begins with an overview of the concepts of data mining and CRM, followed by a discussion of evolution, characteristics, techniques, and applications of both concepts. Next, it integrates the two concepts and illustrates the relationship, benefits, and approaches to implementation, and the limitations of the technologies. Through two studies, we offer a closer look at two data mining techniques: Chi-square Automatic Interaction Detection (CHAID) and Neural Networks. Based on those case studies, CHAID and neural networks are compared and contrasted on the basis of their strengths and weaknesses. Finally, we draw conclusions based on the discussion.2.1. Definition“Data mining‖ is defined as a sophisticated data search capability that uses statistical algorithms to discover patterns and correlations in data [2]. The term is an analogy to gold or coal mining; data mining finds and extracts knowledge (―data nuggets‖) buried in corporate data warehouses, or information that visitors have dropped on a website, most of which can lead to improvements in the understanding and use of the data. The data mining approach is complementary to other data analysis techniques such as statistics, on-line analytical processing (OLAP),spreadsheets, and basic data access. In simple terms, data mining is another way to findmeaning in data.Data mining discovers patterns and relationships hidden in data [3], and is actually part of a larger process called ―knowledge discovery‖ which describes the steps that must be taken to ensure meaningful results. Data mining software does not, however, eliminate the need to know the business, understand the data, or be aware of generalstatistical methods. Data mining does not find patterns and knowledgethat can be trusted automatically without verification. Data mininghelps business analysts to generate hypotheses, but it does not validate the hypotheses. 2.2. The evolution of data miningData mining techniques are the result of a long research and product development process. The origin of data mining lies with the first storage of data on computers, continues with improvements in data access, until today technology allows users to navigate through data in real time. In the evolution from business data to useful information, each step is built on the previous ones. Table 1 shows the evolutionarystages from the perspective of the user.In the first stage, Data Collection, individual sites collected data used to make simple calculations such as summations or averages. Information generated at this step answered business questions relatedto figures derived from data collection sites,such as total revenue oraverage total revenue over a period of time. Specific application programs were created for collecting data and calculations.The second step, Data Access, used databases to store data in a structured format. At this stage, company-wide policies for data collection and reporting of management information were established. Because every business unit conformed to specific requirements or formats, businesses could query the information system regarding branch sales during any specified time period.Once individual figures were known, questions that probed the performance of aggregated sites could be asked. For example, regional sales for a specified period could be calculated. Thanks to multi-dimensional databases, a business could obtain either a global view or drill down to a particular site for comparisons with its peers (Data Navigation). Finally, on-line analytic tools provided real-time feedback and information exchange with collaborating business units (Data Mining). This capability is useful when sales representatives or customer service persons need to retrieve customer information on-line and respond to questions on a real-time basis.Information systems can query past data up to and including the current level of business. Often businesses need to make strategic decisions or implement new policies that better serve their customers. For example, grocery stores redesign their layout to promote moreimpulse purchasing. Telephone companies establish new price structuresto entice customers into placing more calls. Both tasks require an understanding of past customer consumption behavior data in order to identify patterns for making those strategic decisions—and data mining is particularly suited tothis purpose. With the application of advanced algorithms, datamining uncovers knowledge in a vast amount of data and points out possible relationships among the data. Data mining help businesses address questions such as, ―What is likely to happen to Bos ton unit sales next month, and why?‖ Each of the four stages were revolutionary because they allowed new business questions to be answered accuratelyand quickly [4].The core components of data mining technology have been developingfor decades in research areas such as statistics, artificial intelligence, and machine learning. Today, these technologies are mature, and when coupled with relational database systems and a culture of data integration, they create a business environment that can capitalize on knowledge formerly buried within the systems.2.3. Applications of data miningData mining tools take data and construct a representation ofreality in the form of a model. The resulting model describes patterns and relationships present in the data. From a process orientation, data mining activities fall into three general categories(see Fig. 1):Discovery—the process of looking in a database to find hidden patterns without a predetermined idea or hypothesis about what the patterns may be.Predictive Modeling—the process of taking patterns discovered from the database and using them to predict the future.Forensic Analysis—the process of applying the extracted patterns to find anomalous or unusual data elements.Data mining is used to construct six types of models aimed at solving business problems: classification, regression, time series, clustering, association analysis, and sequence discovery [3]. The first two, classification and regression, are used to make predictions, while association and sequence discovery are used to describe behavior. Clustering can be used for either forecasting or description.Companies in various industries can gain a competitive edge bymining their expanding databases for valuable, detailed transaction information. Examples of such uses are provided below.Each of the four applications below makes use of the first two activities of data mining: discovery and predictive modeling. The discovery process, while not mentioned explicitly in the examples (except in the retail description), is used to identify customer segments. This is done through conditional logic, analysis of affinities and associations, and trends and variations. Each of the application categories described below describes some sort of predictive modeling. Each business is interested in predicting the behavior of its customers through the knowledge gained indata mining [5].2.3.1. RetailThrough the use of store-branded credit cards and point-of-sale systems, retailers can keep detailed records of every shopping transaction. This enables them to better understand their various customer segments. Some retail applications include [5]: Performing basket analysis—Also known as affinity analysis, basket analysis reveals which items customers tend to purchase together. This knowledge can improve stocking, store layout strategies, and promotions. Sales forecasting—Examining time-based patterns helps retailers make stocking decisions. If a customer purchases an item today, when are they likely to purchase a complementary item?Database marketing—Retailers can develop profiles of customers with certain behaviors, for example, those who purchase designer labels clothing or those who attend sales. This information can be used to focus cost–effective promotions.Merchandise planning and allocation—When retailers add new stores, they can improvemerchandise planning and allocation by examining patterns in stores with similar demographic characteristics. Retailers can also use data mining to determine the ideal layout for a specific store. 2.3.2. BankingBanks can utilize knowledge discovery for various applications, including [5]: Card marketing—By identifying customer segments, card issuers and acquirers can improve profitability with more effective acquisition and retention programs, targeted product development, and customized pricing.Cardholder pricing and profitability—Card issuers can take advantage of data mining technologyto price their products so as to maximize profit and minimize loss of customers. Includes risk-based pricing.Fraud detection—Fraud is enormously costly. By analyzing past transactions that were later determined to be fraudulent, banks can identify patterns.Predictive life-cycle management—Data mining helps banks predict each custo mer’s lifetimevalue and to service each segment appropriately (for example, offering special deals and discounts).译文:客户关系管理中的数据挖掘技术Chris Rygielski , Jyun-Cheng Wang , David C. Yen摘要近年来，技术的进步让关系营销成为一个现实。
电子商务毕业论文外文翻译---构建数据挖掘在客户关系管理中的应用
英文翻译原文题目：Building Data Mining Applications for CRM出处：New York McGraw-HillProfessional,2000.Berson,Alex.;Smith,Stephen;Thearling, Kurt译文题目：构建数据挖掘在客户关系管理中的应用介绍在过去的几年里，公司和他们的客户之间的接触发生了戏剧性的变化。
顾客不再有过去那么高的忠诚度。
结果是，公司发现他们必须更好地了解和理解他们的客户，对于客户的要求和需求也必须更快地响应。
另外，响应的时间必须大大缩短，不能等到让你的客户等地不耐烦的时候才采取措施，那样就太晚了！为了取得成功，公司必须具有前瞻性，及早了解到你的客户需要的到底是什么。
如果现在说店主能够毫不费力地明白他们消费者的需求而且加以快速的响应，那无疑是陈词滥调。
过去的店主能够仅仅凭借自己的记忆记住他们的客户，而且当客人进来的时候知道该怎么做。
不过现在的店主无疑面临着更为严峻的情况：越来越多的消费者、越来越多的产品、越来越多的竞争对手，但是必须在比过去少的多的时间内了解消费者的需求无疑更为困难。
企业做了许多努力来加强与客户之间的联系。
举个例子来说：压缩市场周期。
企业对于客户的统计分析显示，客户的忠诚度在不断地下降。
而对于客户而言，忠诚两个字仿佛是很遥远的事情了。
一个成功的企业必须加强对他们客户的影响力，提供给他们持续的影响力。
另外，需求是随着时间不断变化的，你必须满足不断变化的需求。
如果你不能快速对客户的需求加以反应，你的客户会转向那些能够帮助他们的公司。
市场的成本越来越大，每一样东西的成本都似乎越来越大。
打印、邮资、特别的服务（如果你不提供这些特别的服务，你的竞争者会提供的）消费者希望货物能够满足他们的要求，每一项都符合。
这意味着他们提供的产品数量和供货方式会急剧地增加。
建立数据挖掘应用程序我们必须要意识到重要的一点，数据挖掘只是整个过程的一部分。
数据挖掘_Dataset on Flea Beetles(跳蚤甲虫数据)
Dataset on Flea Beetles(跳蚤甲虫数据)数据摘要：Data were collected on the genus of flea beetle Chaetocnema, which contains three species: concinna (Con), heikertingeri (Hei), and heptapotamica (Hep). Measurements were made on the width and angle of the aedeagus of each beetle. The goal of the original study was to form a classification rule to distinguish the three species.中文关键词：数据挖掘,生物学,跳蚤,甲虫,英文关键词：Data mining,Biology,Flea,Beetles,数据格式：TEXT数据用途：The data can be used for data mining and analysis.数据详细介绍：Dataset on Flea Beetles ∙AbstractData were collected on the genus of flea beetle Chaetocnema, which contains three species: concinna (Con), heikertingeri (Hei), and heptapotamica (Hep). Measurements were made on the width and angle of the aedeagus of each beetle. The goal of the original study was to form a classification rule to distinguish the three species.∙Data DescriptionNumber of cases: 74Variable Names:Width: The maximal width of aedeagus in the forpart (in microns)Angle: The front angle of the aedeagus (1 unit = 7.5 degrees)Species: Species of flea beetle from the genus ChaetocnemaReferenceLubischew, A.A. (1962) On the use of discriminant functions in taxonomy. Biometrics, 18, 455-477. Also found in: Hand, D.J., et al.(1994) A Handbook of Small Data Sets, London: Chapman & Hall, 254-255.数据预览：点此下载完整数据集。
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中英文对照外文翻译文献(文档含英文原文和中文翻译)中英文资料对照外文翻译英文原文Introduction to Data MiningAbstract:Microsoft® SQL Server™ 2005 provides an integrated environment for creating and working with data mining models. This tutorial uses four scenarios, targeted mailing, forecasting, market basket, and sequence clustering, to demonstrate how to use the mining model algorithms, mining model viewers, and data mining tools that are included in this release of SQL Server.IntroductionThe data mining tutorial is designed to walk you through the process of creating data mining models in Microsoft SQL Server 2005. The data mining algorithms and tools in SQL Server 2005 make it easy to build a comprehensive solution for a variety of projects, including market basket analysis, forecasting analysis, and targeted mailing analysis. The scenarios for these solutions areexplained in greater detail later in the tutorial.The most visible components in SQL Server 2005 are the workspaces that you use to create and work with data mining models. The online analytical processing (OLAP) and data mining tools are consolidated into two working environments: Business Intelligence Development Studio and SQL Server Management Studio. Using Business Intelligence Development Studio, you can develop an Analysis Services project disconnected from the server. When the project is ready, you can deploy it to the server. You can also work directly against the server. The main function of SQL Server Management Studio is to manage the server. Each environment is described in more detail later in this introduction. For more information on choosing between the two environments, see "Choosing Between SQL Server Management Studio and Business Intelligence Development Studio" in SQL Server Books Online.All of the data mining tools exist in the data mining editor. Using the editor you can manage mining models, create new models, view models, compare models, and create predictions based on existing models.After you build a mining model, you will want to explore it, looking for interesting patterns and rules. Each mining model viewer in the editor is customized to explore models built with a specific algorithm. For more information about the viewers, see "Viewing a Data Mining Model" in SQL Server Books Online.Often your project will contain several mining models, so before you can use a model to create predictions, you need to be able to determine which model is the most accurate. For this reason, the editor contains a model comparison tool called the Mining Accuracy Chart tab. Using this tool you can compare the predictive accuracy of your models and determine the best model.To create predictions, you will use the Data Mining Extensions (DMX) language. DMX extends SQL, containing commands to create, modify, and predict against mining models. For more information about DMX, see "Data Mining Extensions (DMX) Reference" in SQL Server Books Online. Because creating a prediction can be complicated, the data mining editor contains a tool called Prediction Query Builder, which allows you to build queries using a graphical interface. You can also view the DMX code that is generated by the query builder.Just as important as the tools that you use to work with and create data mining models are the mechanics by which they are created. The key to creating a mining model is the data mining algorithm. The algorithm finds patterns in the data that you pass it, and it translates them into a mining model — it is the engine behind the process.Some of the most important steps in creating a data mining solution are consolidating, cleaning, and preparing the data to be used to create the mining models. SQL Server 2005 includes the Data Transformation Services (DTS) working environment, which contains tools that you can use to clean, validate, and prepare your data. For more information on using DTS in conjunction with a data mining solution, see "DTS Data Mining Tasks and Transformations" in SQL Server Books Online.In order to demonstrate the SQL Server data mining features, this tutorial uses a new sample database called AdventureWorksDW. The database is included with SQL Server 2005, and it supports OLAP and data mining functionality. In order to make the sample database available, you need to select the sample database at the installati on time in the “Advanced” dialog for component selection.Adventure WorksAdventureWorksDW is based on a fictional bicycle manufacturing company named Adventure Works Cycles. Adventure Works produces and distributes metal and composite bicycles to North American, European, and Asian commercial markets. The base of operations is located in Bothell, Washington with 500 employees, and several regional sales teams are located throughout their market base.Adventure Works sells products wholesale to specialty shops and to individuals through the Internet. For the data mining exercises, you will work with the AdventureWorksDW Internet sales tables, which contain realistic patterns that work well for data mining exercises.For more information on Adventure Works Cycles see "Sample Databases and Business Scenarios" in SQL Server Books Online.Database DetailsThe Internet sales schema contains information about 9,242 customers. These customers live in six countries, which are combined into three regions:North America (83%)Europe (12%)Australia (7%)The database contains data for three fiscal years: 2002, 2003, and 2004.The products in the database are broken down by subcategory, model, and product.Business Intelligence Development StudioBusiness Intelligence Development Studio is a set of tools designed for creating business intelligence projects. Because Business Intelligence Development Studio was created as an IDE environment in which you can create a complete solution, you work disconnected from the server. You can change your data mining objects as much as you want, but the changes are not reflected on the server until after you deploy the project.Working in an IDE is beneficial for the following reasons:The Analysis Services project is the entry point for a business intelligence solution. An Analysis Services project encapsulates mining models and OLAP cubes, along with supplemental objects that make up the Analysis Services database. From Business Intelligence Development Studio, you can create and edit Analysis Services objects within a project and deploy the project tothe appropriate Analysis Services server or servers.If you are working with an existing Analysis Services project, you can also use Business Intelligence Development Studio to work connected the server. In this way, changes are reflected directly on the server without having to deploy the solution.SQL Server Management StudioSQL Server Management Studio is a collection of administrative and scripting tools for working with Microsoft SQL Server components. This workspace differs from Business Intelligence Development Studio in that you are working in a connected environment where actions are propagated to the server as soon as you save your work.After the data has been cleaned and prepared for data mining, most of the tasks associated with creating a data mining solution are performed within Business Intelligence Development Studio. Using the Business Intelligence Development Studio tools, you develop and test the data mining solution, using an iterative process to determine which models work best for a given situation. When the developer is satisfied with the solution, it is deployed to an Analysis Services server. From this point, the focus shifts from development to maintenance and use, and thus SQL Server Management Studio. Using SQL Server Management Studio, you can administer your database and perform some of the same functions as in Business Intelligence Development Studio, such as viewing, and creating predictions from mining models.Data Transformation ServicesData Transformation Services (DTS) comprises the Extract, Transform, and Load (ETL) tools in SQL Server 2005. These tools can be used to perform some of the most important tasks in data mining: cleaning and preparing the data for model creation. In data mining, you typically perform repetitive data transformations to clean the data before using the data to train a mining model. Using the tasks and transformations in DTS, you can combine data preparation and model creation into a single DTS package.DTS also provides DTS Designer to help you easily build and run packages containing all of the tasks and transformations. Using DTS Designer, you can deploy the packages to a server and run them on a regularly scheduled basis. This is useful if, for example, you collect data weekly data and want to perform the same cleaning transformations each time in an automated fashion.You can work with a Data Transformation project and an Analysis Services project together as part of a business intelligence solution, by adding each project to a solution in Business Intelligence Development Studio.Mining Model AlgorithmsData mining algorithms are the foundation from which mining models are created. The variety of algorithms included in SQL Server 2005 allows you to perform many types of analysis.For more specific information about the algorithms and how they can be adjusted using parameters, see "Data Mining Algorithms" in SQL Server Books Online.Microsoft Decision TreesThe Microsoft Decision Trees algorithm supports both classification and regression and it works well for predictive modeling. Using the algorithm, you can predict both discrete and continuous attributes.In building a model, the algorithm examines how each input attribute in the dataset affects the result of the predicted attribute, and then it uses the input attributes with the strongest relationship to create a series of splits, called nodes. As new nodes are added to the model, a tree structure begins to form. The top node of the tree describes the breakdown of the predicted attribute over the overall population. Each additional node is created based on the distribution of states of the predicted attribute as compared to the input attributes. If an input attribute is seen to cause the predicted attribute to favor one state over another, a new node is added to the model. The model continues to grow until none of the remaining attributes create a split that provides an improved prediction over the existing node. The model seeks to find a combination of attributes and their states that creates a disproportionate distribution of states in the predicted attribute, therefore allowing you to predict the outcome of the predicted attribute.Microsoft ClusteringThe Microsoft Clustering algorithm uses iterative techniques to group records from a dataset into clusters containing similar characteristics. Using these clusters, you can explore the data, learning more about the relationships that exist, which may not be easy to derive logically through casual observation. Additionally, you can create predictions from the clustering model created by the algorithm. For example, consider a group of people who live in the same neighborhood, drive the same kind of car, eat the same kind of food, and buy a similar version of a product. This is a cluster of data. Another cluster may include people who go to the same restaurants, have similar salaries, and vacation twice a year outside the country. Observing how these clusters are distributed, you can better understand how the records in a dataset interact, as well as how that interaction affects the outcome of a predicted attribute.Microsoft Naïve BayesThe Microsoft Naïve Bayes algorithm quickly builds mining models that can be used for classification and prediction. It calculates probabilities for each possible state of the input attribute, given each state of the predictable attribute, which can later be used to predict an outcome of the predicted attribute based on the known input attributes. The probabilities used to generate the model are calculated and stored during the processing of the cube. The algorithm supports only discrete or discretized attributes, and it considers all input attributes to be independent. TheMicrosoft Naïve Bayes algorithm produces a simple mining model that can be considered a starting point in the data mining process. Because most of the calculations used in creating the model are generated during cube processing, results are returned quickly. This makes the model a good option for exploring the data and for discovering how various input attributes are distributed in the different states of the predicted attribute.Microsoft Time SeriesThe Microsoft Time Series algorithm creates models that can be used to predict continuous variables over time from both OLAP and relational data sources. For example, you can use the Microsoft Time Series algorithm to predict sales and profits based on the historical data in a cube.Using the algorithm, you can choose one or more variables to predict, but they must be continuous. You can have only one case series for each model. The case series identifies the location in a series, such as the date when looking at sales over a length of several months or years.A case may contain a set of variables (for example, sales at different stores). The Microsoft Time Series algorithm can use cross-variable correlations in its predictions. For example, prior sales at one store may be useful in predicting current sales at another store.Microsoft Neural NetworkIn Microsoft SQL Server 2005 Analysis Services, the Microsoft Neural Network algorithm creates classification and regression mining models by constructing a multilayer perceptron network of neurons. Similar to the Microsoft Decision Trees algorithm provider, given each state of the predictable attribute, the algorithm calculates probabilities for each possible state of the input attribute. The algorithm provider processes the entire set of cases , iteratively comparing the predicted classification of the cases with the known actual classification of the cases. The errors from the initial classification of the first iteration of the entire set of cases is fed back into the network, and used to modify the network's performance for the next iteration, and so on. You can later use these probabilities to predict an outcome of the predicted attribute, based on the input attributes. One of the primary differences between this algorithm and the Microsoft Decision Trees algorithm, however, is that its learning process is to optimize network parameters toward minimizing the error while the Microsoft Decision Trees algorithm splits rules in order to maximize information gain. The algorithm supports the prediction of both discrete and continuous attributes.Microsoft Linear RegressionThe Microsoft Linear Regression algorithm is a particular configuration of the Microsoft Decision Trees algorithm, obtained by disabling splits (the whole regression formula is built in a single root node). The algorithm supports the prediction of continuous attributes.Microsoft Logistic RegressionThe Microsoft Logistic Regression algorithm is a particular configuration of the Microsoft Neural Network algorithm, obtained by eliminating the hidden layer. The algorithm supports the prediction of both discrete andcontinuous attributes.)中文译文数据挖掘技术简介摘要：微软® SQL Server™2005中提供用于创建和使用数据挖掘模型的集成环境的工作。