数据挖掘技术英语论文
大数据领域英文作文
大数据领域英文作文英文回答：In the field of big data, the amount of informationthat can be processed and analyzed is massive. This has led to significant advancements in various industries, such as healthcare, finance, and marketing. Big data allows companies and organizations to gain valuable insights and make informed decisions based on patterns and trends.For example, in the healthcare industry, big data analysis can be used to identify patterns in patient data and predict disease outbreaks. This can help healthcare providers allocate resources more efficiently and develop preventive measures. In finance, big data can be used to detect fraudulent activities and make more accurate predictions in stock markets. In marketing, big data analysis can help companies understand consumer behavior and tailor their advertising strategies accordingly.Furthermore, big data has also revolutionized the waywe conduct research and make scientific discoveries. With the help of big data analytics, researchers can analyzelarge datasets and uncover hidden patterns and correlations. This has led to breakthroughs in various scientific fields, such as genomics, astronomy, and climate science.Moreover, big data has also contributed to the development of artificial intelligence (AI) and machine learning. By analyzing large datasets, AI algorithms can learn and improve their performance over time. This has led to the development of AI-powered technologies, such asvirtual assistants, autonomous vehicles, and personalized recommendations.In conclusion, big data has had a profound impact on various industries and has opened up new possibilities for innovation and growth. The ability to process and analyze massive amounts of information has allowed companies and organizations to make more informed decisions and gain valuable insights. Additionally, big data has also contributed to advancements in research, AI, and machinelearning. Overall, big data is shaping the future and will continue to play a crucial role in our society.中文回答：在大数据领域，可以处理和分析的信息量是巨大的。
大数据技术的作文英语
大数据技术的作文英语Big Data Technology。
With the rapid development of information technology, the era of big data has arrived. Big data technology refers to the collection, storage, processing, and analysis of large and complex data sets to extract valuable insights and knowledge. It has become an indispensable tool for various fields including business, science, healthcare, finance, and more. In this essay, I will delve into the significance, applications, and challenges of big data technology.First and foremost, big data technology plays a crucial role in extracting valuable insights from massive volumes of data. Traditional data processing methods are often inadequate to handle the sheer volume, velocity, andvariety of data generated in today's digital world. Big data technologies such as Hadoop, Spark, and NoSQL databases provide scalable and efficient solutions to storeand process vast amounts of data. These technologies enable organizations to gain deeper insights into customer behavior, market trends, and operational efficiency.One of the significant applications of big data technology is in business and marketing. Companies can analyze customer data to understand their preferences, purchasing behavior, and sentiment towards products or services. This enables personalized marketing campaigns, targeted advertising, and product recommendations, leading to better customer engagement and increased sales. For example, e-commerce giant Amazon utilizes big dataanalytics to recommend products based on users' browsing and purchasing history, resulting in a significant increase in sales revenue.Moreover, big data technology has revolutionized healthcare by facilitating data-driven decision-making and personalized medicine. Healthcare providers can analyze electronic health records, medical imaging, and genomic data to identify patterns, diagnose diseases, and recommend personalized treatment plans. This leads to better patientoutcomes, reduced healthcare costs, and improved population health management. For instance, IBM's Watson Health platform leverages big data analytics to assist healthcare professionals in diagnosing and treating cancer patients more effectively.Furthermore, big data technology has immense potential in scientific research and discovery. Scientists can analyze large datasets generated from experiments, simulations, and observations to uncover new insights and knowledge across various disciplines. This includes areas such as climate modeling, genomics, particle physics, and astronomy. For example, the Large Hadron Collider (LHC) generates petabytes of data from particle collisions, which are analyzed using big data techniques to discover new particles and understand the fundamental laws of physics.Despite its numerous benefits, big data technology also poses several challenges. One of the primary challenges is data privacy and security. As large volumes of sensitive data are collected and stored, there is a risk of data breaches, unauthorized access, and misuse of personalinformation. Ensuring data privacy and compliance with regulations such as GDPR (General Data Protection Regulation) and HIPAA (Health Insurance Portability and Accountability Act) is crucial to maintain trust and integrity in the use of big data.Another challenge is the need for skilled professionals proficient in big data technologies and data analytics. There is a growing demand for data scientists, data engineers, and data analysts who can manage and analyze large datasets effectively. However, there is a shortage of talent with the necessary skills and expertise, leading to a competitive job market and recruitment challenges for organizations.In conclusion, big data technology has revolutionized the way we collect, store, process, and analyze data, enabling unprecedented insights and opportunities across various industries. From business and marketing to healthcare and scientific research, big data technology has transformed the way we make decisions, innovate, and solve complex problems. However, it also poses challenges such asdata privacy, security, and skills shortage. Addressing these challenges will be crucial in harnessing the full potential of big data technology for the benefit of society.。
数据挖掘论文医学数据论文：医学数据挖掘综述
数据挖掘论文医学数据论文：医学数据挖掘综述摘要：医学数据挖掘是提高医学信息管理水平，为疾病的诊断和治疗提供科学准确的决策，促进医疗发展的需要。
该文主要介绍了医学数据的特点，医学数据挖掘的发展状况和应用的技术方法，同时展望了数据挖掘技术在医学领域的应用前景。
关键词：数据挖掘；医学数据；神经网络；关联规则summary of medical data miningwang ju-qin(department of computer technology, wuxi institute of technology, wuxi 214121, china)abstract: medical data mining is necessary for improving the management level of medical information, providing scientific decision-making for the diagnosis and treatment of disease, and promoting the development of medicine. this paper mainly introduces the characters of mining medical data, the application and methods used in medicine, and also the application prospect medical field is outlined.key words: data mining; medical data; neural network; association rules1 数据挖掘的产生1.1 产生背景在当今信息化和网络化的社会条件下，随着计算机、数据库技术的迅速发展以及数据库管理系统的广泛应用，各行各业都开始采用计算机以及相应的信息技术进行管理和运营，由此积累了大量的数据资料；另外，互联网的发展更是为我们带来了海量的数据和信息。
人工智能技术论文英文
人工智能技术论文英文Artificial Intelligence: A Comprehensive Exploration of Modern Technologies and Their ApplicationsThe advent of artificial intelligence (AI) has revolutionized the way we interact with technology, transforming industries and shaping the future of human-computer interaction. This paper delves into the realm of AI, exploring its evolution, current technologies, applications, and the ethical considerations that accompany its rapid advancement.IntroductionArtificial intelligence, a term coined in 1956, has come along way from its initial conceptualization to its current state, where AI systems are capable of performing tasks that typically require human intelligence. The field of AI encompasses a wide range of disciplines, including machine learning, natural language processing, computer vision, and robotics. The integration of AI into various sectors has ledto significant breakthroughs in efficiency, accuracy, and innovation.Historical ContextThe history of AI is marked by periods of optimism and skepticism. The first AI programs were developed in the 1950s, with the Dartmouth Conference in 1956 being a pivotal momentthat set the stage for AI research. The 1960s and 1970s saw the development of the first AI programs, including ELIZA and SHRDLU. However, the field faced a period of stagnation known as the "AI winter" due to unfulfilled promises and lack of funding. It wasn't until the late 1990s and early 2000s that AI research gained momentum again, with the advent of machine learning and the availability of big data.Fundamental ConceptsAt the core of AI are algorithms and computational modelsthat enable machines to learn from data, make decisions, and perform tasks autonomously. Machine learning, a subset of AI, involves the development of algorithms that can learn from and make predictions or decisions based on data. Deep learning, a subset of machine learning, uses neural networks with many layers to model complex patterns in data.Current TechnologiesThe current landscape of AI technologies is diverse and includes:1. Machine Learning Platforms: These platforms provide the tools and frameworks for developers to build and train AI models.2. Natural Language Processing (NLP): NLP enables machines to understand, interpret, and generate human language.3. Computer Vision: This technology allows machines to interpret and analyze visual information from the world.4. Robotics: AI-powered robots can perform tasks that requirephysical manipulation and movement.5. Expert Systems: These systems use AI to simulate the decision-making ability of a human expert.ApplicationsAI has found its way into numerous applications across various industries:1. Healthcare: AI is used for diagnosis, treatment planning, and personalized medicine.2. Finance: AI technologies are employed for fraud detection, algorithmic trading, and risk management.3. Transportation: Autonomous vehicles and smart traffic systems are powered by AI.4. Retail: AI enhances customer experience through personalized recommendations and inventory management.5. Education: Adaptive learning systems powered by AI cater to individual learning needs.Challenges and Ethical ConsiderationsAs AI continues to advance, it brings with it a set of challenges and ethical considerations:1. Bias and Fairness: AI systems can inherit and amplify the biases present in their training data, leading to unfair outcomes.2. Privacy: The use of AI in data analysis raises concerns about individual privacy and data protection.3. Job Displacement: The automation of tasks by AI has thepotential to displace jobs, leading to economic and social implications.4. Transparency and Explainability: The complexity of AI models can make it difficult to understand how they arrive at certain decisions.The Future of AILooking ahead, AI is poised to become more integrated intoour daily lives, with advancements in areas such as general AI, which aims to create machines that can perform any intellectual task that a human being can. The development ofAI also calls for a collaborative approach between technologists, policymakers, and society to ensure its responsible and beneficial use.ConclusionArtificial intelligence stands as a testament to human ingenuity and our relentless pursuit of innovation. While it presents numerous opportunities for societal advancement, it also poses significant challenges that must be addressed. As we move forward, it is crucial to foster a balanced approach that harnesses the potential of AI while mitigating its risks. The journey of AI is not just about creating intelligent machines; it is about shaping a future that is inclusive, ethical, and beneficial for all.In conclusion, the field of AI is dynamic and ever-evolving.It holds the promise of transforming our world in ways we are only beginning to understand. As we continue to explore anddevelop AI technologies, it is imperative that we do so with a keen eye on their societal impact, ensuring that they serve to enhance and enrich our lives in a manner that is responsible and sustainable.。
写一篇关于大数据专业的英语作文
写一篇关于大数据专业的英语作文英文回答：The field of Big Data has emerged as a transformative force in today's digital landscape. Defined as the vast and complex collection of data that exceeds the capacity of traditional data processing tools, Big Data holds immense potential for organizations to extract valuable insights and make data-driven decisions.The significance of Big Data lies in its three key attributes: volume, velocity, and variety. The sheer volume of data generated by various sources, including social media, IoT devices, and enterprise systems, poses challenges in storage and processing. The velocity, or rate at which new data streams in, requires real-time analysis to capture its time-sensitive value. Finally, the variety of data types, including structured, semi-structured, and unstructured data, necessitates specialized techniques for data integration and analysis.The benefits of leveraging Big Data are multifaceted.It enables organizations to:Enhance decision-making: Data-driven insights empower businesses to make informed decisions, optimize processes, and predict market trends.Improve customer experience: By analyzing customer data, organizations can personalize experiences, anticipate preferences, and address pain points.Detect fraud and security breaches: Big Data analytics can detect anomalies and identify suspicious patterns in financial transactions and security logs.Drive innovation: Data-powered research and development foster innovation by identifying new opportunities and developing cutting-edge products and services.Increase operational efficiency: Data analysis canuncover inefficiencies, streamline operations, and optimize resource allocation.To harness the full potential of Big Data, organizations must invest in robust data management and analytics infrastructure. This includes scalable storage solutions, powerful processing engines, and specialized analytics tools. Additionally, skilled data scientists and engineers who possess expertise in data mining, statistical modeling, and machine learning are essential for extracting meaningful insights from vast data volumes.The impact of Big Data is not confined to the business world. It has profound implications for society as a whole:Healthcare: Big Data analytics can improve patient outcomes, optimize drug discovery, and personalize medical treatments.Environmental monitoring: Data from sensors and satellites helps track environmental changes, predict natural disasters, and protect ecosystems.Transportation: Data analysis can optimize traffic flow, improve vehicle efficiency, and enhancetransportation safety.Government: Big Data supports data-driven policymaking, fraud detection, and anti-corruption measures.As technology continues to advance, the volume, velocity, and variety of Big Data will only increase. This presents both opportunities and challenges fororganizations and society alike. By embracing the potential of Big Data and investing in data management and analytics capabilities, we can unlock unprecedented insights anddrive innovation for the benefit of future generations.中文回答：大数据专业。
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。
数据挖掘论文精选5篇论文
数据挖掘论⽂精选5篇论⽂数据挖掘论⽂精选5篇论⽂ 数据挖掘⼀： 题⽬：数据挖掘技术在神经根型颈椎病⽅剂研究中的优势及应⽤进展 关键词：数据挖掘技术; 神经根型颈椎病; ⽅剂; 综述; 1 数据挖掘技术简介 数据挖掘技术[1] (Knowledge Discovery in Datebase, KKD) , 是⼀种新兴的信息处理技术, 它融汇了⼈⼯智能、模式别、模糊数学、数据库、数理统计等多种技术⽅法, 专门⽤于海量数据的处理, 从⼤量的、不完全的、有噪声的、模糊的、随机的数据集中, 提取隐含在其中的、⼈们事先不知道的、但⼜是潜在的有⽤的信息和知识, 其⽬的是发现规律⽽不是验证假设。
数据挖掘技术主要适⽤于庞⼤的数据库的研究, 其特点在于:基于数据分析⽅法⾓度的分类, 其本质属于观察性研究, 数据来源于⽇常诊疗⼯作,应⽤的技术较传统研究更先进, 分析⼯具、理论模型与传统研究区别较⼤。
其操作步骤包括[2]:选择数据, 数据处理, 挖掘分析, 结果解释, 其中结果解释是数据挖掘技术研究的关键。
其⽅法包括分类、聚类、关联、序列、决策树、贝斯⽹络、因⼦、辨别等分析[3], 其结果通常表⽰为概念、规则、规律、模式、约束、可视化等形式图[4]。
当今数据挖掘技术的⽅向主要在于:特定数据挖掘, ⾼效挖掘算法, 提⾼结果的有效性、确定性和表达性, 结果的可视化, 多抽象层上的交互式数据挖掘, 多元数据挖掘及数据的安全性和保密性。
因其优势和独特性被运⽤于多个领域中, 且结果运⽤后取得显着成效, 因此越来越多的中医⽅剂研究者将其运⽤于⽅剂中药物的研究。
2 数据挖掘术在神经根型颈椎病治⽅研究中的优势 中医对于神经根型颈椎病的治疗准则为辨证论治, 从古⾄今神经根型颈椎病的中医证型有很多, 其治⽅是集中医之理、法、⽅、药为⼀体的数据集合, 具有以“⽅-药-证”为核⼼的多维结构。
⽅剂配伍本质上表现为⽅与⽅、⽅与药、药与药、药与剂量, 以及⽅药与证、病、症交叉错综的关联与对应[5], ⽽中医⽅剂讲究君⾂佐使的配伍, 药物有升降沉浮, 四⽓五味及归经之别, 对于神经根型颈椎病的治疗, 治⽅中药物的种类、炮制⽅法、⽤量、⽤法等都是千变万化的, ⽽这些海量、模糊、看似随机的药物背后隐藏着对临床有⽤的信息和规律, 但这些⼤数据是⽆法在可承受的时间范围内可⽤常规软件⼯具进⾏捕捉、管理和处理的, 是需要⼀个新处理模式才能具有更强的决策⼒、洞察⼒和流程优化能⼒, ⽽数据挖掘技术有可能从这些海量的的数据中发现新知识, 揭⽰背后隐藏的关系和规则, 并且对未知的情况进⾏预测[6]。
数据挖掘技术应用论文
浅析数据挖掘技术的应用摘要：作为数据库研究、开发和应用最活跃的一个分支，数据挖掘技术的研究日益蓬勃的发展。
从信息处理的角度来看，数据挖掘技术在帮助人们分析数据和理解数据，并帮助人们基于丰富的数据作出决策上起到了非常重要的角色。
从大量数据中以平凡的方法发现有用的知识是数据挖掘技术的核心，也是今后在各个领域中发展的核心技术。
关键词：数据挖掘；功能；应用中图分类号：tp311.13 文献标识码：a文章编号：1007-9599(2011)24-0000-01analysis of data mining technology applicationzhang pengyu,duan shiliu(henan polytechnic,zhengzhou450000,china)abstract:as the database research,development and application of the most active branch of data mining technology research booming development. from the perspective of information processing,data mining technology to help people analyze data and understand the data,and help people make decisions based on the wealth of data has played a very important role. from large amounts of data in an extraordinary way to discover useful knowledge is the core of data mining technology,but also the future development invarious fields in the core technology.keywords:data mining;function;application一、数据挖掘概述近年来，数据挖掘引起了信息产业界和整个社会的极大关注，其主要原因是存在可以广泛使用大量数据，并且迫切需要将这些数据转换成有用的信息和知识。
数据挖掘论文
数据挖掘论文数据挖掘（data mining）是指在数据库中发掘有价值的数据信息的过程。
同时，数据挖掘在各个领域，如交通、金融、医疗等，也有着广泛的应用。
数据挖掘算法通过对数据进行分析和挖掘，帮助人们发现数据之间的关联性，提供了获取潜在信息的方式。
在数据挖掘领域，论文是评价研究人员和研究机构的重要标准。
数据挖掘论文的主要内容包括数据挖掘方法、数据挖掘工具、数据挖掘应用等。
在数据挖掘方法方面，论文通常会涉及到分类、聚类、关联分析、异常检测等多种数据挖掘算法。
比如，在分类算法方面，有朴素贝叶斯、决策树、支持向量机等算法，这些算法在分类任务中有着广泛的应用。
在聚类算法方面，K均值、层次聚类和密度聚类等算法是数据挖掘领域中经典的聚类算法。
关联分析算法则能够帮助人们挖掘数据中隐藏的关联规则。
论文中还会介绍各种数据挖掘工具的使用，如R语言、Python语言、Weka等工具。
这些工具能够大大提高数据挖掘的效率和准确率，因此也是数据挖掘领域必不可少的工具。
另外，数据挖掘在各种领域的应用也是研究的热点之一。
在交通领域中，通过数据挖掘技术可以对交通流量进行预测、交通路线优化等；在金融领域中，数据挖掘可用于信用评分、交易预测等；在医疗领域中，数据挖掘常用于疾病诊断、流行病预测等。
近年来，随着深度学习技术的发展，深度学习也成为了数据挖掘研究的重要方向之一。
深度学习算法通过神经网络的模式识别能力，可以对数据进行更加准确的分析和挖掘。
例如，在图像识别领域中，卷积神经网络已经成为了图像识别的主流算法。
在数据挖掘领域，论文的撰写和发表对于研究者和机构的影响重大。
优秀的数据挖掘论文可以帮助人们更好地了解数据挖掘领域的最新技术和应用，同时也为各个领域的数据挖掘应用提供了更好的支持。
总之，数据挖掘领域的论文具有很高的实用价值和研究意义，与各行各业的发展都息息相关。
对于想要在数据挖掘领域深耕的人们，认真撰写、发表数据挖掘论文，则是必须掌握的技能之一。
[论文]人工智能在电子商务中的应用
![[论文]人工智能在电子商务中的应用](https://img.taocdn.com/s3/m/2470b3bbdc3383c4bb4cf7ec4afe04a1b071b079.png)
人工智能在电子商务中的应用摘要：人工智能技术和电子商务的飞速发展推动了全球科技经济领域的进步，基于人工智能技术的电子商务更趋向完美和成熟。
随着电子商务的不断发展和人工智能技术的不断完善，两者在各个领域、各个层次的相互融合将更加密切。
作为各自的成功因素，电子商务和人工智能技术的融合必将成为一种关键技术。
关键词：电子商务；人工智能；数据挖掘1 引言随着Internet和相关Web技术的发展电子商务的交易模式产生了极大的改变，新技术的引入引发了电子商务新交易模式的研究热潮。
对基于互联网的在线交易模式，许多国内外的著名专家学者进行了深入研究，并在网络环境中迁移传统商务交易模型方面取得大量的研究成果（Pinker E J,Scigmann A,V akrat Y,2003),从而也不断促进了电子商务的发展和应用。
近期，电子商务网络交易模式及其应用主要集中在在线招标投标、网上拍卖以及电子谈判等领域。
人工智能是研究使机器具备人所具有的智能功能的一门高新技术学科。
其目的是模拟、延伸和扩展人的智能，以实现某些脑力劳动的自动化。
实质化，它是开拓计算机应用、研制新一代计算机和扩展计算机应用领域的技术基础，也是探索人脑奥秘的重要科学途径。
人工智能、原子能技术、空间技术，被称为20世纪的三大尖端科技。
进入21世纪后，人工智能仍是适应信息时代需求的关键技术之一。
明确上述人工智能定以后，不难指明智能化与电脑化的区别。
“人工智能”是指，采用人工智能理论、方法和技术，并具有某种或某些拟人智能特性或功能。
有电脑后不一定采用人工智能方法，也不一定具有人工智能特性，故不一定能被称为“智能化”。
电子商务，源于英文ELECTRONIC COMMERCE，简写为EC，指的是利用简单、快捷、低成本的电子通讯方式，买卖双方不谋面地进行各种商贸活动。
其内容包含两个方面：一是电子方式；二是商贸活动。
电子商务可以通过多种电子通讯方式来完成，但是，现在人们所探讨的电子商务主要是以EDI（电子数据交换）和INTER来完成的。
关于大数据领域的英文作文
关于大数据领域的英文作文英文回答：Big data has become a hot topic in recent years, revolutionizing various industries and transforming the way we live and work. It refers to the massive amounts of data that are generated and collected from various sources, such as social media, sensors, and online transactions. This data is then analyzed to extract valuable insights and make informed decisions. The impact of big data can be seen in areas such as healthcare, finance, marketing, and transportation.In the healthcare industry, big data has the potential to improve patient care and outcomes. By analyzing large volumes of patient data, including medical records, test results, and genetic information, healthcare providers can identify patterns and trends that can help in early detection and treatment of diseases. For example, by analyzing data from wearable devices, doctors can monitor apatient's heart rate, blood pressure, and other vital signs in real-time, allowing for immediate intervention if necessary.In the finance industry, big data is used for fraud detection and risk assessment. Banks and financial institutions analyze large volumes of transaction data to identify suspicious activities and prevent fraudulent transactions. By using advanced analytics and machine learning algorithms, they can detect patterns and anomalies that may indicate fraudulent behavior. For instance, if a customer suddenly makes a large number of transactions in a short period of time, it could be a sign of identity theft or credit card fraud.In the marketing field, big data is used to personalize and target advertising campaigns. Companies collect and analyze customer data to understand their preferences, behavior, and purchasing patterns. This information allows them to deliver personalized advertisements and offers to individual customers, increasing the chances of conversion. For example, if a customer frequently searches for andpurchases running shoes online, a sports apparel companycan target them with relevant ads and promotions.In the transportation industry, big data is used to optimize routes, reduce congestion, and improve safety. By analyzing data from GPS devices, traffic cameras, and weather sensors, transportation authorities can identify traffic patterns and make informed decisions to alleviate congestion. For example, if a traffic camera detects atraffic jam on a particular road, authorities can redirect traffic to alternative routes and notify drivers in real-time, reducing travel time and improving overall efficiency.In conclusion, big data has revolutionized various industries and has the potential to transform the way welive and work. It has enabled healthcare providers to improve patient care, financial institutions to detect fraud, marketers to personalize advertisements, and transportation authorities to optimize routes. With the continuous advancement of technology and the increasing amount of data being generated, the role of big data will only continue to grow in the future.中文回答：大数据近年来成为一个热门话题，它正在革新各个行业，改变我们的生活和工作方式。
数据挖掘算法案例三篇
数据挖掘算法案例三篇篇一：数据挖掘算法经典案例国际权威的学术组织theIEEEInternationalConferenceonDataMining(ICDM)20XX年12月评选出了数据挖掘领域的十大经典算法：C4.5,k-Means,SVM,Apriori,EM,PageRank,AdaBoost,kNN,NaiveBayes,andCART 。
不仅仅是选中的十大算法，其实参加评选的18种算法，实际上随便拿出一种来都可以称得上是经典算法，它们在数据挖掘领域都产生了极为深远的影响。
（一）C4.5C4.5算法是机器学习算法中的一种分类决策树算法,其核心算法是ID3算法。
C4.5算法继承了ID3算法的优点，并在以下几方面对ID3算法进行了改进：1.用信息增益率来选择属性，克服了用信息增益选择属性时偏向选择取值多的属性的不足；2.在树构造过程中进行剪枝；3.能够完成对连续属性的离散化处理；4.能够对不完整数据进行处理。
C4.5算法有如下优点：产生的分类规则易于理解，准确率较高。
其缺点是：在构造树的过程中，需要对数据集进行多次的顺序扫描和排序，因而导致算法的低效。
（二）Thek-meansalgorithm即K-Means算法k-meansalgorithm算法是一个聚类算法，把n的对象根据他们的属性分为k个分割，k<n。
它与处理混合正态分布的最大期望算法很相似，因为他们都试图找到数据中自然聚类的中心。
它假设对象属性来自于空间向量，并且目标是使各个群组内部的均方误差总和最小。
（三）Supportvectormachines支持向量机，英文为SupportVectorMachine，简称SV机（论文中一般简称SVM）。
它是一种監督式學習的方法，它广泛的应用于统计分类以及回归分析中。
支持向量机将向量映射到一个更高维的空间里，在这个空间里建立有一个最大间隔超平面。
在分开数据的超平面的两边建有两个互相平行的超平面。
数据挖掘技术在高校教学管理论文
数据挖掘技术在高校教学管理中的应用摘要：高校在教学和管理中逐渐聚集了相当数量的数据资料，要充分的使用好这些数据资料研究技术对这些数据进行分析和挖掘，从中找出相对于教育教学和监督管理有关的知识，帮助管理者更好的做出决策，与此同时也能够促进学校合理设置课程、制定培养方案和有效管理学生，从而推动学校的全面发展。
abstract： colleges and universities has accumulated a large amount of data gradually in the teaching and management，it is necessary to make full use of these data material and carry on the analysis and mining to these data， and finds out the knowledge which is relative to the education and teaching as well as the supervision and administration， help managers to make better decisions， at the same time， it can also promote the school to set up reasonable curriculum，formulate training plan and effectively manage students， so as to promote the all-round development of the school.关键词：数据挖掘；教学管理；关联规则key words： data mining；teaching management；association rules中图分类号：tp39 文献标识码：a 文章编号：1006-4311（2012）34-0200-020 引言随着信息技术的快速发展，多数的高等院校都建立了教务管理系统，高校的日常管理基本上全面实现了信息化和网络化。
大数据作文开头结尾
大数据作文开头结尾英文回答：Introduction:In the era of big data, the world is witnessing a paradigm shift in how information is collected, analyzed, and utilized. The exponential growth of data, coupled with advancements in technology, has revolutionized various aspects of our lives. From business to healthcare, from education to entertainment, the impact of big data is profound and far-reaching.Body:Big data refers to the massive volumes of structured and unstructured data generated at an unprecedented pace from various sources such as social media, sensors, and online transactions. This avalanche of data presents both challenges and opportunities. On one hand, organizationsstruggle to manage, process, and derive insights from this vast amount of information. On the other hand, big data analytics enables businesses to gain valuable insights into consumer behavior, market trends, and operational efficiency.For instance, e-commerce giants like Amazon and Alibaba leverage big data analytics to personalize product recommendations for millions of users, thereby enhancing the shopping experience and increasing sales. Similarly, healthcare institutions utilize big data to predict disease outbreaks, optimize treatment plans, and improve patient outcomes. In education, educators analyze student performance data to tailor teaching strategies and interventions, fostering better learning outcomes.Furthermore, big data plays a crucial role in decision-making processes across industries. By analyzing historical data and real-time information, businesses can make informed decisions regarding resource allocation, risk management, and strategic planning. Governments utilize big data analytics for policy formulation, law enforcement, andpublic service optimization.Conclusion:In conclusion, the era of big data presents immense opportunities for innovation, growth, and societal advancement. However, harnessing the full potential of big data requires overcoming challenges such as data privacy concerns, cybersecurity threats, and ethical considerations. By leveraging advanced analytics tools, adopting data-driven approaches, and fostering collaboration, we can unlock the transformative power of big data to create a better future for all.中文回答：开头：在大数据时代，世界正在经历信息收集、分析和利用方式的转变。
数据挖掘ACM论文翻译-附录为英文原文
基于自然语言的Apriori关联规则的视觉挖掘方法摘要：抽象-可视化数据挖掘技术可以以图形方式向用户展示数据挖掘过程，从而使用户更易于理解挖掘过程及其结果，而且在数据挖掘中也非常重要。
然而，现在大多数视觉数据挖掘都是通过可视化的结果而进行的。
同时，它不适用于关联规则的可视化处理的图形显示。
鉴于上述缺点，本文采用自然语言处理方法，以自然语言视觉地进行Apriori关联规则的整体挖掘过程，包括数据预处理，挖掘过程和挖掘结果的可视化显示为用户提供了一套具有更多感知和更易于理解的特征的集成方案关键字：apriori 关联规则数据挖掘可视化1 引言视觉数据挖掘技术是可视化技术和数据挖掘技术的结合。
使用计算机图形、图像处理技术等方法将数据挖掘的源数据，中间结果和最终挖掘结果转换成易于理解的图形或图像，然后进行贯穿的理论，方法和技术交互式处理。
根据数据挖掘应用中可视化的不同阶段，数据挖掘的可视化可以分为源数据可视化，挖掘过程可视化和结果可视化。
（1）源数据可视化源数据可视化方法在数据挖掘之前，以可视化的形式将整个数据集呈现给用户。
目的是使用户能够快速找到有趣的地区，从而实现挖掘目标和目标的下一步。
（2）过程可视化过程可视化实现起来相当复杂。
主要有两种方法- 一种是在采矿过程中可视化地呈现中间结果，并使用户根据中间结果的反馈方便地调整参数和约束。
另一种方法是以图标和流程图的形式保持整个数据挖掘过程，根据用户可以观察数据源，数据集成，清理和预处理过程以及采矿结果的存储和可视化等等。
（3）结果可视化数据挖掘结果可视化是指在采矿过程结束时以图形和图像的形式描述挖掘结果或知识，以提高用户对结果的理解，并使用户更好地评估和利用采矿结果。
2、国外家庭视觉数据挖掘研究状况目前，视觉数据挖掘技术的研究在国内外都处于起步阶段，如何使用可视化技术来显示利用各种数据挖掘算法生成后的模型。
该方向的主要研究内容是通过一些特殊视觉图形中的关联规则、决策树和聚类等算法向用户显示生成的结果，以帮助用户更好地了解结果数据挖掘模型。
计算机病毒数据库数据挖掘论文
计算机病毒数据库的数据挖掘研究摘要：本文论述了计算机病毒数据库的数据挖掘方法，为检测、识别计算机病毒以及预防和清除计算机病毒，提供了理论依据和较好的办法。
abstract： this paper discusses method of data mining of computer virus database，which provides theoretical basis and preferable means for detecting and identifying computer virus and preventing and removing computer virus.关键词：计算机病毒；数据挖掘；病毒数据库key words： computer virus；data mining；virus database 中图分类号：tp392 文献标识码：a 文章编号：1006-4311（2012）31-0199-020 引言计算机病毒的危害现在无人不晓，无人不知。
但要防止危害、清除危害，确实是一件很困难的事情。
首先，计算机病毒的种类繁多，五花八门，无奇不有。
传播渠道广泛，来源于网络的、来源于其他渠道的比比皆是。
它严重威胁着国家的安全、各个行业的信息安全和人们生活安全。
给人们的正常工作和生活造成很大的危害和影响。
为了消除计算机病毒对人们的危害，使人们生活和工作能正常进行，信息传送畅通无阻，信息资料得以安全保存和保密。
计算机反病毒技术必须超前发展。
除了各种操作系统具有反病毒技术的功能外，对于未来计算机病毒能够进行预测、识别和防范。
因此，对于未来的病毒进行预测研究、识别研究及防范研究，是一件当务之急的任务。
显然，计算机病毒研究工作具有战略意义和现实意义。
1 建立反病毒数据库建立反病毒数据库，对病毒样本数据进行研究、挖掘、分析，进行归类，找出规律，并对未来的计算机病毒进行预测，制定出各种可能的反病毒策略、方案、手段、技术措施。
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Good evening, ladies and gentlemen. I’m very glad to stand here and give you a short speech. Today I would introduce data mining technology to you. What is the data mining technology and what’s advantage and disadvantage. Now let's talk about this.
Data mining refers to "Extracting implicit unknown valuable information from the data in the past” or “a scientific extracting information from a large amount of data or databases”, In general,it needs strict steps to be taken.including understanding, aquistion, intergration, data cleaning, assumptions and interpretation.
By using these steps, we could get implicit and valuable information from the data. However, in spite of these complete steps, there are still many shortcomings.
First of all, the operator has many problems in its development, such as the target market segmentation is not clear,the demand of data mining and evaluation of information is not enough; product planning and management are difficult to meet the customer information needs; the attraction to partners is a little less, and it has not yet formed a win-win value chain; in the level of operation management and business process, the ability of sales team and group informatization service are not adapted to the development of business.In a word, there’re still have a lot of things to be solved. It needs excellent statistics and technology. It
also needs greater power of refining and summary.
Secondly,it’s easy to listen only by the data.”let the data speak”is not wrong, but we should keep it in mind that :next,parties! If the data and tools can solve the problem,what should people do? The data itself can only help analysts to find what are significant results,but it can’t tell you whether the result is right or wrong.So it also requires us to check up the relevant information seriously in case of being cheated by the data. Thirdly, Related to data mining,it also involves privacy issues, for example: an employer can access medical records to screen out those who have diabetes or serious heart disease, which is aimed to reduce the insurance expenditure. However, this approach will lead to ethical and legal issues.
Data mining of government and commerce may involve in the national security or commercial confidentiality issues . It is also a big challenge to confidentiality.. In this aspect,it need the user obey social morals and government strengthen regulation.
All in all,every technology has its own advantages and disadvantages. We need to learn to recognize it and how to use it effectively. In order to create greater benefits for mankind.we still have many things to be discovered about data mining. That’s
all,thanks for your listening.
,。