Abstract Mining Quantitative Association Rules in Large Relational Tables
大数据技术与应用考试 选择题 63题
1. 大数据的4V特征不包括以下哪一项？A. VolumeB. VelocityC. VarietyD. Visibility2. Hadoop生态系统中，用于存储结构化和半结构化数据的组件是？A. HDFSB. HiveC. HBaseD. Pig3. 在数据仓库中，ETL过程指的是什么？A. Extract, Transform, LoadB. Encrypt, Transfer, LoadC. Extract, Transfer, LoadD. Encrypt, Transform, Load4. 以下哪个不是NoSQL数据库的类型？A. Key-ValueB. Column-FamilyC. DocumentD. Relational5. 数据挖掘中的分类算法不包括以下哪一项？A. Decision TreesB. Neural NetworksC. ClusteringD. Support Vector Machines6. 在Hadoop中，MapReduce的主要作用是什么？A. 数据存储B. 数据处理C. 数据查询D. 数据可视化7. 以下哪个工具不是用于大数据分析的？A. RB. PythonC. ExcelD. Spark8. 在数据预处理中，数据清洗的主要目的是什么？A. 增加数据量B. 减少数据量C. 提高数据质量D. 提高数据速度9. 以下哪个不是大数据处理框架？A. FlinkB. KafkaC. StormD. Docker10. 在数据可视化中，热力图主要用于展示什么？A. 数据分布B. 数据关系C. 数据趋势D. 数据密度11. 以下哪个是大数据安全的关键技术？A. 数据加密B. 数据压缩C. 数据存储D. 数据传输12. 在数据分析中，OLAP是什么的缩写？A. Online Analytical ProcessingB. Online Application ProcessingC. Offline Analytical ProcessingD. Offline Application Processing13. 以下哪个不是数据仓库的特点？A. 面向主题B. 集成性C. 时变性D. 实时性14. 在数据挖掘中，关联规则挖掘主要用于发现什么？A. 数据模式B. 数据异常C. 数据关系D. 数据趋势15. 以下哪个不是大数据的应用领域？A. 金融B. 医疗C. 教育D. 娱乐16. 在Hadoop中，YARN的主要作用是什么？A. 数据存储B. 资源管理C. 数据处理D. 数据查询17. 以下哪个不是数据湖的特点？A. 存储原始数据B. 存储结构化数据C. 灵活的数据结构D. 支持多种数据类型18. 在数据分析中，数据集市是什么？A. 数据仓库的子集B. 数据仓库的超集C. 独立的数据仓库D. 数据仓库的备份19. 以下哪个不是数据治理的关键组成部分？A. 数据质量B. 数据安全C. 数据存储D. 数据政策20. 在数据挖掘中，聚类算法主要用于什么？A. 数据分类B. 数据分组C. 数据预测D. 数据关联21. 以下哪个不是大数据处理的关键技术？A. 数据采集B. 数据存储C. 数据分析D. 数据打印22. 在数据可视化中，散点图主要用于展示什么？A. 数据分布B. 数据关系C. 数据趋势D. 数据密度23. 以下哪个不是大数据分析的步骤？A. 数据收集B. 数据清洗C. 数据存储D. 数据分析24. 在数据仓库中，维度表和事实表的关系是什么？A. 一对一B. 一对多C. 多对一D. 多对多25. 以下哪个不是数据挖掘的应用场景？A. 市场篮分析B. 客户细分C. 风险评估D. 数据备份26. 在Hadoop中，HDFS的主要作用是什么？A. 数据存储B. 数据处理C. 数据查询D. 数据可视化27. 以下哪个不是数据湖的优势？A. 存储原始数据B. 灵活的数据结构C. 支持多种数据类型D. 实时数据处理28. 在数据分析中，数据立方体是什么？A. 数据仓库的子集B. 数据仓库的超集C. 数据仓库的备份D. 数据仓库的多维数据模型29. 以下哪个不是数据治理的目标？A. 提高数据质量B. 确保数据安全C. 提高数据速度D. 确保数据合规30. 在数据挖掘中，异常检测主要用于发现什么？A. 数据模式B. 数据异常C. 数据关系D. 数据趋势31. 以下哪个不是大数据的应用优势？A. 提高决策效率B. 降低成本C. 提高数据质量D. 提高服务质量32. 在Hadoop中，MapReduce的主要优势是什么？A. 数据存储B. 数据处理C. 数据查询D. 数据可视化33. 以下哪个不是数据湖的挑战？A. 数据管理B. 数据安全C. 数据处理D. 数据备份34. 在数据分析中，数据集成的目的是什么？A. 提高数据质量B. 确保数据安全C. 提高数据速度D. 确保数据合规35. 以下哪个不是数据挖掘的步骤？A. 数据收集B. 数据清洗C. 数据存储D. 数据分析36. 在数据仓库中，数据集成的关键技术是什么？A. 数据采集B. 数据存储C. 数据分析D. 数据清洗37. 以下哪个不是大数据分析的工具？A. RB. PythonC. ExcelD. Photoshop38. 在数据可视化中，折线图主要用于展示什么？A. 数据分布B. 数据关系C. 数据趋势D. 数据密度39. 以下哪个不是大数据处理的关键技术？A. 数据采集B. 数据存储C. 数据分析D. 数据打印40. 在数据仓库中，数据集成的关键技术是什么？A. 数据采集B. 数据存储C. 数据分析D. 数据清洗41. 以下哪个不是大数据分析的工具？A. RB. PythonC. ExcelD. Photoshop42. 在数据可视化中，折线图主要用于展示什么？A. 数据分布B. 数据关系C. 数据趋势D. 数据密度43. 以下哪个不是大数据处理的关键技术？A. 数据采集B. 数据存储C. 数据分析D. 数据打印44. 在数据仓库中，数据集成的关键技术是什么？A. 数据采集B. 数据存储C. 数据分析D. 数据清洗45. 以下哪个不是大数据分析的工具？A. RB. PythonC. ExcelD. Photoshop46. 在数据可视化中，折线图主要用于展示什么？A. 数据分布B. 数据关系C. 数据趋势47. 以下哪个不是大数据处理的关键技术？A. 数据采集B. 数据存储C. 数据分析D. 数据打印48. 在数据仓库中，数据集成的关键技术是什么？A. 数据采集B. 数据存储C. 数据分析D. 数据清洗49. 以下哪个不是大数据分析的工具？A. RB. PythonC. ExcelD. Photoshop50. 在数据可视化中，折线图主要用于展示什么？A. 数据分布B. 数据关系C. 数据趋势D. 数据密度51. 以下哪个不是大数据处理的关键技术？A. 数据采集B. 数据存储C. 数据分析D. 数据打印52. 在数据仓库中，数据集成的关键技术是什么？A. 数据采集B. 数据存储C. 数据分析D. 数据清洗53. 以下哪个不是大数据分析的工具？A. RB. PythonC. ExcelD. Photoshop54. 在数据可视化中，折线图主要用于展示什么？A. 数据分布C. 数据趋势D. 数据密度55. 以下哪个不是大数据处理的关键技术？A. 数据采集B. 数据存储C. 数据分析D. 数据打印56. 在数据仓库中，数据集成的关键技术是什么？A. 数据采集B. 数据存储C. 数据分析D. 数据清洗57. 以下哪个不是大数据分析的工具？A. RB. PythonC. ExcelD. Photoshop58. 在数据可视化中，折线图主要用于展示什么？A. 数据分布B. 数据关系C. 数据趋势D. 数据密度59. 以下哪个不是大数据处理的关键技术？A. 数据采集B. 数据存储C. 数据分析D. 数据打印60. 在数据仓库中，数据集成的关键技术是什么？A. 数据采集B. 数据存储C. 数据分析D. 数据清洗61. 以下哪个不是大数据分析的工具？A. RB. PythonC. ExcelD. Photoshop62. 在数据可视化中，折线图主要用于展示什么？A. 数据分布B. 数据关系C. 数据趋势D. 数据密度63. 以下哪个不是大数据处理的关键技术？A. 数据采集B. 数据存储C. 数据分析D. 数据打印答案1. D2. B3. A4. D5. C6. B7. C8. C9. D10. D11. A12. A13. D14. C15. D16. B17. B18. A19. C20. B21. D22. A23. C24. B25. D26. A27. D28. D29. C30. B31. C32. B33. D34. A35. C36. D37. D38. C39. D40. D41. D42. C43. D44. D45. D46. C47. D48. D49. D50. C51. D52. D53. D54. C55. D56. D57. D58. C59. D60. D61. D62. C63. D。
模糊云资源调度的CMAPSO算法
模糊云资源调度的CMAPSO算法作者：李成严，宋月，马金涛来源：《哈尔滨理工大学学报》2022年第01期摘要：针对多目标云资源调度问题，以优化任务的总完成时间和总执行成本为目标，采用模糊数学的方法，建立了模糊云资源调度模型。
利用协方差矩阵能够解决非凸性问题的优势，采取协方差进化策略对种群进行初始化，并提出了一种混合智能优化算法CMAPSO算法（covariance matrix adaptation evolution strategy particle swarm optimization，CMAPSO ），并使用该算法对模糊云资源调度模型进行求解。
使用Cloudsim仿真平台随机生成云计算资源调度的数据，对CMAPSO算法进行测试，实验结果证明了CMAPSO算法对比PSO算法（particle wwarm optimization），在寻优能力方面提升28%，迭代次数相比提升20%，并且具有良好的负载均衡性能。
关键词：云计算;任务调度;粒子群算法; 协方差矩阵进化策略DOI：10.15938/j.jhust.2022.01.005中图分类号： TP399 文献标志码： A 文章编号： 1007-2683（2022）01-0031-09CMAPSO Algorithm for Fuzzy Cloud Resource SchedulingLI Chengyan，SONG Yue，MA Jintao（School of Computer Science and Technology， Harbin University of Science and Technology， Harbin 150080，China）Abstract：Aiming at the multiobjective cloud resource scheduling problem， with the goal of optimizing the total completion time and total execution cost of the task， a fuzzy cloud resource scheduling model is established using the method of fuzzy mathematics. Utilizing the advantage of the covariance matrix that can solve the nonconvexity problem， adopting the covariance evolution strategy to initialize the population， a hybrid intelligent optimization algorithm CMAPSO algorithm （covariance matrix adaptation evolution strategy particle swarm optimization，CMAPSO） is proposed to solve the fuzzy cloud resource scheduling model. The Cloudsim simulation platform was used to randomly generate cloud computing resource scheduling data， and the CMAPSO algorithm was tested. The experimental results showed that compared with the PSO algorithm （particle swarm optimization）， the optimization capability of CMAPSO algorithm is increased by 28%， the number of iterations of CMAPSO algorithm is increased by 20%， and it has good load balancing performance.Keywords：cloud computing; task scheduling; particle swarm algorithm; covariance matrix adaptation evolution strategy0引言云計算是一种商业计算的模型和服务模式[1]，而云计算资源调度的主要目的是将网络上的资源进行统一的管理和调式，再给予用户服务调用。
BP神经网络在砂岩储层流体识别中的应用
BP神经网络在砂岩储层流体识别中的应用摘要：莫北地区侏罗纪三工河组，岩性主要是中、细砂岩，属低孔低渗储层，所以储层流体性质的识别是该地区急需解决的问题。
针对常规测井储层识别准确率不佳的情况，提出了bp神经网络这种数学方法进行储层的油、气、水、干层的识别。
提出43个试油层段的测井曲线特征值，以对流体性质敏感并且在交会图上具有比较明显区分度的密度值（den）、孔隙度（por）、电阻率值（rt）和含水饱和度值（sw）作为输入向量，经程序训练判别准确率达到满足的要求后根据得到的权值、阈值编写神经网络预测的程序挂接在测井解释软件中，从而实现了bp神经网络在储层中的自动化识别。
关键词：bp神经网络流体识别测井解释中图分类号：p631.8 文献标识码：a 文章编号：1672-3791（2013）02（b）-0011-021 bp神经网络基本原理1.2 bp神经网络构造根据与储层特征相关的测井响应值本文采用4-10-5的神经网络结构，即输入层采用4个节点对应的是4种测井响应值，单隐含层采用10个节点，输出层为4个节点对应的是4种储层类别，分别对应是油层、油水同层、水层、干层并且依次表示为1类、2类、3类、4类，用数字0与1代表其属性，油层表示为[1，0，0，0]，油水同层表示为[0，1，0，0]，水层表示为[0，0，1，0]，干层表示为[0，0，0，1]。
随机初始化神经网络权值和阈值，然后根据误差反馈不断的调整权值阈值。
其中隐含层节点数和迭代资料的选择是在matlab中用数据反复演练，直到得出能够满足实际储层解释判断正确率后得出的准确值。
本文中采用附加动量方法算法网络进行网络收敛弥补神经网络算法的不足，使其学习过程收敛加快。
网络实际输出是一个四维向量，其数值在0和1之间，我们取向量中最接近1所在的数组编号作为类别的输出。
2 bp神经网络实际应用2.1 建立模型网络训练样本设计的好坏，是直接关系到网络识别储层准确率的一个关键环节。
数据挖掘原理、算法及应用章 (8)
第8章 复杂类型数据挖掘 1） 以Arc/info基于矢量数据模型的系统为例， 为了将空间
数据存入计算机， 首先， 从逻辑上将空间数据抽象为不同的 专题或层， 如土地利用、 地形、 道路、 居民区、 土壤单 元、 森林分布等， 一个专题层包含区域内地理要素的位置和 属性数据。 其次， 将一个专题层的地理要素或实体分解为点、 线、 面目标， 每个目标的数据由空间数据、 属性数据和拓 扑数据组成。
第8章 复杂类型数据挖掘 2. 空间数据具体描述地理实体的空间特征、 属性特征。 空
间特征是指地理实体的空间位置及其相互关系； 属性特征表 示地理实体的名称、 类型和数量等。 空间对象表示方法目前 采用主题图方法, 即将空间对象抽象为点、 线、 面三类， 根据这些几何对象的不同属性， 以层（Layer）为概念组织、 存储、 修改和显示它们， 数据表达分为矢量数据模型和栅格 数据模型两种。
第8章 复杂类型数据挖掘图Fra bibliotek-5 综合图层
第8章 复杂类型数据挖掘
图8-4 栅格数据模型
第8章 复杂类型数据挖掘
3. 虽然空间数据查询和空间挖掘是有区别的， 但是像其他数 据挖掘技术一样， 查询是挖掘的基础和前提， 因此了解空间 查询及其操作有助于掌握空间挖掘技术。
由于空间数据的特殊性， 空间操作相对于非空间数据要 复杂。 传统的访问非空间数据的选择查询使用的是标准的比 较操作符： “>”、 “<”、 “≤ ”、 “≥ ”、 “≠ ”。 而空间选择是一种在空间数据上的选择查询， 要用到空间操 作符.包括接近、 东、 西、 南、 北、 包含、 重叠或相交 等。
不同的实体之间进行空间性操作的时候， 经常需要在属性之 间进行一些转换。 如果非空间属性存储在关系型数据库中， 那么一种可行的存储策略是利用非空间元组的属性存放指向相 应空间数据结构的指针。 这种关系中的每个元组代表的是一 个空间实体。
人工智能基础(习题卷9)
人工智能基础(习题卷9)第1部分：单项选择题，共53题，每题只有一个正确答案,多选或少选均不得分。
1.[单选题]由心理学途径产生，认为人工智能起源于数理逻辑的研究学派是（ ）A)连接主义学派B)行为主义学派C)符号主义学派答案:C解析:2.[单选题]一条规则形如：，其中“←"右边的部分称为(___)A)规则长度B)规则头C)布尔表达式D)规则体答案:D解析:3.[单选题]下列对人工智能芯片的表述，不正确的是（）。
A)一种专门用于处理人工智能应用中大量计算任务的芯片B)能够更好地适应人工智能中大量矩阵运算C)目前处于成熟高速发展阶段D)相对于传统的CPU处理器，智能芯片具有很好的并行计算性能答案:C解析:4.[单选题]以下图像分割方法中，不属于基于图像灰度分布的阈值方法的是( )。
A)类间最大距离法B)最大类间、内方差比法C)p-参数法D)区域生长法答案:B解析:5.[单选题]下列关于不精确推理过程的叙述错误的是（ ）。
A)不精确推理过程是从不确定的事实出发B)不精确推理过程最终能够推出确定的结论C)不精确推理过程是运用不确定的知识D)不精确推理过程最终推出不确定性的结论答案:B解析:6.[单选题]假定你现在训练了一个线性SVM并推断出这个模型出现了欠拟合现象，在下一次训练时，应该采取的措施是（）0A)增加数据点D)减少特征答案:C解析:欠拟合是指模型拟合程度不高，数据距离拟合曲线较远，或指模型没有很好地捕 捉到数据特征，不能够很好地拟合数据。
可通过增加特征解决。
7.[单选题]以下哪一个概念是用来计算复合函数的导数？A)微积分中的链式结构B)硬双曲正切函数C)softplus函数D)劲向基函数答案:A解析:8.[单选题]相互关联的数据资产标准，应确保()。
数据资产标准存在冲突或衔接中断时，后序环节应遵循和适应前序环节的要求，变更相应数据资产标准。
A)连接B)配合C)衔接和匹配D)连接和配合答案:C解析:9.[单选题]固体半导体摄像机所使用的固体摄像元件为( )。
组合数据加密方法的研究毕业论文
学科分类号110 黑龙江科技大学本科学生毕业论文题目组合数据加密方法的研究The Research of Combination DataEncryption Method姓名徐朋学号090524010223院（系）理学院专业、年级数学与应用数学09-2班指导教师张太发2015年6月12日摘要针对吕家坨井田深部开采的矿山压力问题，在区域地质构造分析的基础上，采用空芯包体测量方法，…………………………………………………………………………………………………………本文……………………………………………………………………好的效果。
…………………………………………………………………………………………………………关键词判别分析聚类分析注：关键字数量3~5个，中间空格分隔AbstractIn previous Taxonomy, it classification methods were introduced to the real life of classification issues. ……………………………………………………….. ………………………………………………………………………………………. ………………………………………..The paper …………………………………………………statistics of datas for quick computing………………………………………………………………………………………………………………………………………………………….…………………………………………Keywords Discriminant analysis Cluster analysis注：关键字与中文关键字一一对应，词组之间2个字符分隔，词组内单词之间1个字符分隔目录摘要........................................................................................... 错误!未定义书签。
基于多特征自适应融合的区块链异常交易检测方法
2021年5月Journal on Communications May 2021 第42卷第5期通信学报V ol.42No.5基于多特征自适应融合的区块链异常交易检测方法朱会娟1,2，陈锦富1,2，李致远1,2，殷尚男1,2（1. 江苏大学计算机科学与通信工程学院，江苏镇江 212013；2. 江苏省工业网络安全技术重点实验室，江苏镇江 212013）摘 要：针对智能检测模型的性能受限于原始数据（特征）表达能力的问题，设计了一种残差网络结构ResNet-32用于挖掘区块链交易特征间隐含的关联关系，自动学习包含丰富语义信息的高层抽象特征。
虽然浅层特征区分能力弱，但更忠于原始交易细节的描述，如何充分利用两者的优势是提升异常交易检测性能的关键，因此提出了特征融合方法自适应地桥接高层抽象特征与原始特征之间的鸿沟，自动去除其噪声和冗余信息，并挖掘两者的交叉特征信息获得最具区分力的特征。
最后，结合以上方法提出区块链异常交易检测模型（BATDet），并通过Elliptic数据集验证了所提模型在区块链异常交易检测领域的有效性。
关键词：区块链；残差网络；异常检测；Logistic回归中图分类号：TP18文献标识码：ADOI: 10.11959/j.issn.1000−436x.2021030Block-chain abnormal transaction detection methodbased on adaptive multi-feature fusionZHU Huijuan1,2, CHEN Jinfu1,2, LI Zhiyuan1,2, YIN Shangnan1,21. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China2. Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Zhenjiang 212013, ChinaAbstract: Aiming at the problem that the performance of intelligent detection models was limited by the representation ability of original data (features), a residual network structure ResNet-32 was designed to automatically mine the intricate association relationship between original features, so as to actively learn the high-level abstract features with rich seman-tic information. Low-level features were more transaction content descriptive, although their distinguishing ability was weaker than that of the high-level features. How to integrate them together to obtain complementary advantages was the key to improve the detection performance. Therefore, multi feature fusion methods were proposed to bridge the gap be-tween the two kinds of features. Moreover, these fusion methods can automatically remove the noise and redundant in-formation from the integrated features and further absorb the cross information, to acquire the most distinctive features.Finally, block-chain abnormal transaction detection model (BATDet) was proposed based on the above presented me-thods, and its effectiveness in the abnormal transaction detection is verified.Keywords: block-chain, residual network, abnormal detection, Logistic regression1引言科技的飞速发展促使金融行业从实体金融走向互联网金融，反洗钱的外部环境和内在逻辑均发生了深刻而复杂的变化。
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From Data Mining to Knowledge Discovery in Databases
s Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media atten-tion of late. What is all the excitement about?This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges in-volved in real-world applications of knowledge discovery, and current and future research direc-tions in the field.A cross a wide variety of fields, data arebeing collected and accumulated at adramatic pace. There is an urgent need for a new generation of computational theo-ries and tools to assist humans in extracting useful information (knowledge) from the rapidly growing volumes of digital data. These theories and tools are the subject of the emerging field of knowledge discovery in databases (KDD).At an abstract level, the KDD field is con-cerned with the development of methods and techniques for making sense of data. The basic problem addressed by the KDD process is one of mapping low-level data (which are typically too voluminous to understand and digest easi-ly) into other forms that might be more com-pact (for example, a short report), more ab-stract (for example, a descriptive approximation or model of the process that generated the data), or more useful (for exam-ple, a predictive model for estimating the val-ue of future cases). At the core of the process is the application of specific data-mining meth-ods for pattern discovery and extraction.1This article begins by discussing the histori-cal context of KDD and data mining and theirintersection with other related fields. A briefsummary of recent KDD real-world applica-tions is provided. Definitions of KDD and da-ta mining are provided, and the general mul-tistep KDD process is outlined. This multistepprocess has the application of data-mining al-gorithms as one particular step in the process.The data-mining step is discussed in more de-tail in the context of specific data-mining al-gorithms and their application. Real-worldpractical application issues are also outlined.Finally, the article enumerates challenges forfuture research and development and in par-ticular discusses potential opportunities for AItechnology in KDD systems.Why Do We Need KDD?The traditional method of turning data intoknowledge relies on manual analysis and in-terpretation. For example, in the health-careindustry, it is common for specialists to peri-odically analyze current trends and changesin health-care data, say, on a quarterly basis.The specialists then provide a report detailingthe analysis to the sponsoring health-care or-ganization; this report becomes the basis forfuture decision making and planning forhealth-care management. In a totally differ-ent type of application, planetary geologistssift through remotely sensed images of plan-ets and asteroids, carefully locating and cata-loging such geologic objects of interest as im-pact craters. Be it science, marketing, finance,health care, retail, or any other field, the clas-sical approach to data analysis relies funda-mentally on one or more analysts becomingArticlesFALL 1996 37From Data Mining to Knowledge Discovery inDatabasesUsama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth Copyright © 1996, American Association for Artificial Intelligence. All rights reserved. 0738-4602-1996 / $2.00areas is astronomy. Here, a notable success was achieved by SKICAT ,a system used by as-tronomers to perform image analysis,classification, and cataloging of sky objects from sky-survey images (Fayyad, Djorgovski,and Weir 1996). In its first application, the system was used to process the 3 terabytes (1012bytes) of image data resulting from the Second Palomar Observatory Sky Survey,where it is estimated that on the order of 109sky objects are detectable. SKICAT can outper-form humans and traditional computational techniques in classifying faint sky objects. See Fayyad, Haussler, and Stolorz (1996) for a sur-vey of scientific applications.In business, main KDD application areas includes marketing, finance (especially in-vestment), fraud detection, manufacturing,telecommunications, and Internet agents.Marketing:In marketing, the primary ap-plication is database marketing systems,which analyze customer databases to identify different customer groups and forecast their behavior. Business Week (Berry 1994) estimat-ed that over half of all retailers are using or planning to use database marketing, and those who do use it have good results; for ex-ample, American Express reports a 10- to 15-percent increase in credit-card use. Another notable marketing application is market-bas-ket analysis (Agrawal et al. 1996) systems,which find patterns such as, “If customer bought X, he/she is also likely to buy Y and Z.” Such patterns are valuable to retailers.Investment: Numerous companies use da-ta mining for investment, but most do not describe their systems. One exception is LBS Capital Management. Its system uses expert systems, neural nets, and genetic algorithms to manage portfolios totaling $600 million;since its start in 1993, the system has outper-formed the broad stock market (Hall, Mani,and Barr 1996).Fraud detection: HNC Falcon and Nestor PRISM systems are used for monitoring credit-card fraud, watching over millions of ac-counts. The FAIS system (Senator et al. 1995),from the U.S. Treasury Financial Crimes En-forcement Network, is used to identify finan-cial transactions that might indicate money-laundering activity.Manufacturing: The CASSIOPEE trou-bleshooting system, developed as part of a joint venture between General Electric and SNECMA, was applied by three major Euro-pean airlines to diagnose and predict prob-lems for the Boeing 737. To derive families of faults, clustering methods are used. CASSIOPEE received the European first prize for innova-intimately familiar with the data and serving as an interface between the data and the users and products.For these (and many other) applications,this form of manual probing of a data set is slow, expensive, and highly subjective. In fact, as data volumes grow dramatically, this type of manual data analysis is becoming completely impractical in many domains.Databases are increasing in size in two ways:(1) the number N of records or objects in the database and (2) the number d of fields or at-tributes to an object. Databases containing on the order of N = 109objects are becoming in-creasingly common, for example, in the as-tronomical sciences. Similarly, the number of fields d can easily be on the order of 102or even 103, for example, in medical diagnostic applications. Who could be expected to di-gest millions of records, each having tens or hundreds of fields? We believe that this job is certainly not one for humans; hence, analysis work needs to be automated, at least partially.The need to scale up human analysis capa-bilities to handling the large number of bytes that we can collect is both economic and sci-entific. Businesses use data to gain competi-tive advantage, increase efficiency, and pro-vide more valuable services to customers.Data we capture about our environment are the basic evidence we use to build theories and models of the universe we live in. Be-cause computers have enabled humans to gather more data than we can digest, it is on-ly natural to turn to computational tech-niques to help us unearth meaningful pat-terns and structures from the massive volumes of data. Hence, KDD is an attempt to address a problem that the digital informa-tion era made a fact of life for all of us: data overload.Data Mining and Knowledge Discovery in the Real WorldA large degree of the current interest in KDD is the result of the media interest surrounding successful KDD applications, for example, the focus articles within the last two years in Business Week , Newsweek , Byte , PC Week , and other large-circulation periodicals. Unfortu-nately, it is not always easy to separate fact from media hype. Nonetheless, several well-documented examples of successful systems can rightly be referred to as KDD applications and have been deployed in operational use on large-scale real-world problems in science and in business.In science, one of the primary applicationThere is an urgent need for a new generation of computation-al theories and tools toassist humans in extractinguseful information (knowledge)from the rapidly growing volumes ofdigital data.Articles38AI MAGAZINEtive applications (Manago and Auriol 1996).Telecommunications: The telecommuni-cations alarm-sequence analyzer (TASA) wasbuilt in cooperation with a manufacturer oftelecommunications equipment and threetelephone networks (Mannila, Toivonen, andVerkamo 1995). The system uses a novelframework for locating frequently occurringalarm episodes from the alarm stream andpresenting them as rules. Large sets of discov-ered rules can be explored with flexible infor-mation-retrieval tools supporting interactivityand iteration. In this way, TASA offers pruning,grouping, and ordering tools to refine the re-sults of a basic brute-force search for rules.Data cleaning: The MERGE-PURGE systemwas applied to the identification of duplicatewelfare claims (Hernandez and Stolfo 1995).It was used successfully on data from the Wel-fare Department of the State of Washington.In other areas, a well-publicized system isIBM’s ADVANCED SCOUT,a specialized data-min-ing system that helps National Basketball As-sociation (NBA) coaches organize and inter-pret data from NBA games (U.S. News 1995). ADVANCED SCOUT was used by several of the NBA teams in 1996, including the Seattle Su-personics, which reached the NBA finals.Finally, a novel and increasingly importanttype of discovery is one based on the use of in-telligent agents to navigate through an infor-mation-rich environment. Although the ideaof active triggers has long been analyzed in thedatabase field, really successful applications ofthis idea appeared only with the advent of theInternet. These systems ask the user to specifya profile of interest and search for related in-formation among a wide variety of public-do-main and proprietary sources. For example, FIREFLY is a personal music-recommendation agent: It asks a user his/her opinion of several music pieces and then suggests other music that the user might like (<http:// www.ffl/>). CRAYON(/>) allows users to create their own free newspaper (supported by ads); NEWSHOUND(<http://www. /hound/>) from the San Jose Mercury News and FARCAST(</> automatically search information from a wide variety of sources, including newspapers and wire services, and e-mail rele-vant documents directly to the user.These are just a few of the numerous suchsystems that use KDD techniques to automat-ically produce useful information from largemasses of raw data. See Piatetsky-Shapiro etal. (1996) for an overview of issues in devel-oping industrial KDD applications.Data Mining and KDDHistorically, the notion of finding useful pat-terns in data has been given a variety ofnames, including data mining, knowledge ex-traction, information discovery, informationharvesting, data archaeology, and data patternprocessing. The term data mining has mostlybeen used by statisticians, data analysts, andthe management information systems (MIS)communities. It has also gained popularity inthe database field. The phrase knowledge dis-covery in databases was coined at the first KDDworkshop in 1989 (Piatetsky-Shapiro 1991) toemphasize that knowledge is the end productof a data-driven discovery. It has been popular-ized in the AI and machine-learning fields.In our view, KDD refers to the overall pro-cess of discovering useful knowledge from da-ta, and data mining refers to a particular stepin this process. Data mining is the applicationof specific algorithms for extracting patternsfrom data. The distinction between the KDDprocess and the data-mining step (within theprocess) is a central point of this article. Theadditional steps in the KDD process, such asdata preparation, data selection, data cleaning,incorporation of appropriate prior knowledge,and proper interpretation of the results ofmining, are essential to ensure that usefulknowledge is derived from the data. Blind ap-plication of data-mining methods (rightly crit-icized as data dredging in the statistical litera-ture) can be a dangerous activity, easilyleading to the discovery of meaningless andinvalid patterns.The Interdisciplinary Nature of KDDKDD has evolved, and continues to evolve,from the intersection of research fields such asmachine learning, pattern recognition,databases, statistics, AI, knowledge acquisitionfor expert systems, data visualization, andhigh-performance computing. The unifyinggoal is extracting high-level knowledge fromlow-level data in the context of large data sets.The data-mining component of KDD cur-rently relies heavily on known techniquesfrom machine learning, pattern recognition,and statistics to find patterns from data in thedata-mining step of the KDD process. A natu-ral question is, How is KDD different from pat-tern recognition or machine learning (and re-lated fields)? The answer is that these fieldsprovide some of the data-mining methodsthat are used in the data-mining step of theKDD process. KDD focuses on the overall pro-cess of knowledge discovery from data, includ-ing how the data are stored and accessed, howalgorithms can be scaled to massive data setsThe basicproblemaddressed bythe KDDprocess isone ofmappinglow-leveldata intoother formsthat might bemorecompact,moreabstract,or moreuseful.ArticlesFALL 1996 39A driving force behind KDD is the database field (the second D in KDD). Indeed, the problem of effective data manipulation when data cannot fit in the main memory is of fun-damental importance to KDD. Database tech-niques for gaining efficient data access,grouping and ordering operations when ac-cessing data, and optimizing queries consti-tute the basics for scaling algorithms to larger data sets. Most data-mining algorithms from statistics, pattern recognition, and machine learning assume data are in the main memo-ry and pay no attention to how the algorithm breaks down if only limited views of the data are possible.A related field evolving from databases is data warehousing,which refers to the popular business trend of collecting and cleaning transactional data to make them available for online analysis and decision support. Data warehousing helps set the stage for KDD in two important ways: (1) data cleaning and (2)data access.Data cleaning: As organizations are forced to think about a unified logical view of the wide variety of data and databases they pos-sess, they have to address the issues of map-ping data to a single naming convention,uniformly representing and handling missing data, and handling noise and errors when possible.Data access: Uniform and well-defined methods must be created for accessing the da-ta and providing access paths to data that were historically difficult to get to (for exam-ple, stored offline).Once organizations and individuals have solved the problem of how to store and ac-cess their data, the natural next step is the question, What else do we do with all the da-ta? This is where opportunities for KDD natu-rally arise.A popular approach for analysis of data warehouses is called online analytical processing (OLAP), named for a set of principles pro-posed by Codd (1993). OLAP tools focus on providing multidimensional data analysis,which is superior to SQL in computing sum-maries and breakdowns along many dimen-sions. OLAP tools are targeted toward simpli-fying and supporting interactive data analysis,but the goal of KDD tools is to automate as much of the process as possible. Thus, KDD is a step beyond what is currently supported by most standard database systems.Basic DefinitionsKDD is the nontrivial process of identifying valid, novel, potentially useful, and ultimate-and still run efficiently, how results can be in-terpreted and visualized, and how the overall man-machine interaction can usefully be modeled and supported. The KDD process can be viewed as a multidisciplinary activity that encompasses techniques beyond the scope of any one particular discipline such as machine learning. In this context, there are clear opportunities for other fields of AI (be-sides machine learning) to contribute to KDD. KDD places a special emphasis on find-ing understandable patterns that can be inter-preted as useful or interesting knowledge.Thus, for example, neural networks, although a powerful modeling tool, are relatively difficult to understand compared to decision trees. KDD also emphasizes scaling and ro-bustness properties of modeling algorithms for large noisy data sets.Related AI research fields include machine discovery, which targets the discovery of em-pirical laws from observation and experimen-tation (Shrager and Langley 1990) (see Kloes-gen and Zytkow [1996] for a glossary of terms common to KDD and machine discovery),and causal modeling for the inference of causal models from data (Spirtes, Glymour,and Scheines 1993). Statistics in particular has much in common with KDD (see Elder and Pregibon [1996] and Glymour et al.[1996] for a more detailed discussion of this synergy). Knowledge discovery from data is fundamentally a statistical endeavor. Statistics provides a language and framework for quan-tifying the uncertainty that results when one tries to infer general patterns from a particu-lar sample of an overall population. As men-tioned earlier, the term data mining has had negative connotations in statistics since the 1960s when computer-based data analysis techniques were first introduced. The concern arose because if one searches long enough in any data set (even randomly generated data),one can find patterns that appear to be statis-tically significant but, in fact, are not. Clearly,this issue is of fundamental importance to KDD. Substantial progress has been made in recent years in understanding such issues in statistics. Much of this work is of direct rele-vance to KDD. Thus, data mining is a legiti-mate activity as long as one understands how to do it correctly; data mining carried out poorly (without regard to the statistical as-pects of the problem) is to be avoided. KDD can also be viewed as encompassing a broader view of modeling than statistics. KDD aims to provide tools to automate (to the degree pos-sible) the entire process of data analysis and the statistician’s “art” of hypothesis selection.Data mining is a step in the KDD process that consists of ap-plying data analysis and discovery al-gorithms that produce a par-ticular enu-meration ofpatterns (or models)over the data.Articles40AI MAGAZINEly understandable patterns in data (Fayyad, Piatetsky-Shapiro, and Smyth 1996).Here, data are a set of facts (for example, cases in a database), and pattern is an expres-sion in some language describing a subset of the data or a model applicable to the subset. Hence, in our usage here, extracting a pattern also designates fitting a model to data; find-ing structure from data; or, in general, mak-ing any high-level description of a set of data. The term process implies that KDD comprises many steps, which involve data preparation, search for patterns, knowledge evaluation, and refinement, all repeated in multiple itera-tions. By nontrivial, we mean that some search or inference is involved; that is, it is not a straightforward computation of predefined quantities like computing the av-erage value of a set of numbers.The discovered patterns should be valid on new data with some degree of certainty. We also want patterns to be novel (at least to the system and preferably to the user) and poten-tially useful, that is, lead to some benefit to the user or task. Finally, the patterns should be understandable, if not immediately then after some postprocessing.The previous discussion implies that we can define quantitative measures for evaluating extracted patterns. In many cases, it is possi-ble to define measures of certainty (for exam-ple, estimated prediction accuracy on new data) or utility (for example, gain, perhaps indollars saved because of better predictions orspeedup in response time of a system). No-tions such as novelty and understandabilityare much more subjective. In certain contexts,understandability can be estimated by sim-plicity (for example, the number of bits to de-scribe a pattern). An important notion, calledinterestingness(for example, see Silberschatzand Tuzhilin [1995] and Piatetsky-Shapiro andMatheus [1994]), is usually taken as an overallmeasure of pattern value, combining validity,novelty, usefulness, and simplicity. Interest-ingness functions can be defined explicitly orcan be manifested implicitly through an or-dering placed by the KDD system on the dis-covered patterns or models.Given these notions, we can consider apattern to be knowledge if it exceeds some in-terestingness threshold, which is by nomeans an attempt to define knowledge in thephilosophical or even the popular view. As amatter of fact, knowledge in this definition ispurely user oriented and domain specific andis determined by whatever functions andthresholds the user chooses.Data mining is a step in the KDD processthat consists of applying data analysis anddiscovery algorithms that, under acceptablecomputational efficiency limitations, pro-duce a particular enumeration of patterns (ormodels) over the data. Note that the space ofArticlesFALL 1996 41Figure 1. An Overview of the Steps That Compose the KDD Process.methods, the effective number of variables under consideration can be reduced, or in-variant representations for the data can be found.Fifth is matching the goals of the KDD pro-cess (step 1) to a particular data-mining method. For example, summarization, clas-sification, regression, clustering, and so on,are described later as well as in Fayyad, Piatet-sky-Shapiro, and Smyth (1996).Sixth is exploratory analysis and model and hypothesis selection: choosing the data-mining algorithm(s) and selecting method(s)to be used for searching for data patterns.This process includes deciding which models and parameters might be appropriate (for ex-ample, models of categorical data are differ-ent than models of vectors over the reals) and matching a particular data-mining method with the overall criteria of the KDD process (for example, the end user might be more in-terested in understanding the model than its predictive capabilities).Seventh is data mining: searching for pat-terns of interest in a particular representa-tional form or a set of such representations,including classification rules or trees, regres-sion, and clustering. The user can significant-ly aid the data-mining method by correctly performing the preceding steps.Eighth is interpreting mined patterns, pos-sibly returning to any of steps 1 through 7 for further iteration. This step can also involve visualization of the extracted patterns and models or visualization of the data given the extracted models.Ninth is acting on the discovered knowl-edge: using the knowledge directly, incorpo-rating the knowledge into another system for further action, or simply documenting it and reporting it to interested parties. This process also includes checking for and resolving po-tential conflicts with previously believed (or extracted) knowledge.The KDD process can involve significant iteration and can contain loops between any two steps. The basic flow of steps (al-though not the potential multitude of itera-tions and loops) is illustrated in figure 1.Most previous work on KDD has focused on step 7, the data mining. However, the other steps are as important (and probably more so) for the successful application of KDD in practice. Having defined the basic notions and introduced the KDD process, we now focus on the data-mining component,which has, by far, received the most atten-tion in the literature.patterns is often infinite, and the enumera-tion of patterns involves some form of search in this space. Practical computational constraints place severe limits on the sub-space that can be explored by a data-mining algorithm.The KDD process involves using the database along with any required selection,preprocessing, subsampling, and transforma-tions of it; applying data-mining methods (algorithms) to enumerate patterns from it;and evaluating the products of data mining to identify the subset of the enumerated pat-terns deemed knowledge. The data-mining component of the KDD process is concerned with the algorithmic means by which pat-terns are extracted and enumerated from da-ta. The overall KDD process (figure 1) in-cludes the evaluation and possible interpretation of the mined patterns to de-termine which patterns can be considered new knowledge. The KDD process also in-cludes all the additional steps described in the next section.The notion of an overall user-driven pro-cess is not unique to KDD: analogous propos-als have been put forward both in statistics (Hand 1994) and in machine learning (Brod-ley and Smyth 1996).The KDD ProcessThe KDD process is interactive and iterative,involving numerous steps with many deci-sions made by the user. Brachman and Anand (1996) give a practical view of the KDD pro-cess, emphasizing the interactive nature of the process. Here, we broadly outline some of its basic steps:First is developing an understanding of the application domain and the relevant prior knowledge and identifying the goal of the KDD process from the customer’s viewpoint.Second is creating a target data set: select-ing a data set, or focusing on a subset of vari-ables or data samples, on which discovery is to be performed.Third is data cleaning and preprocessing.Basic operations include removing noise if appropriate, collecting the necessary informa-tion to model or account for noise, deciding on strategies for handling missing data fields,and accounting for time-sequence informa-tion and known changes.Fourth is data reduction and projection:finding useful features to represent the data depending on the goal of the task. With di-mensionality reduction or transformationArticles42AI MAGAZINEThe Data-Mining Stepof the KDD ProcessThe data-mining component of the KDD pro-cess often involves repeated iterative applica-tion of particular data-mining methods. This section presents an overview of the primary goals of data mining, a description of the methods used to address these goals, and a brief description of the data-mining algo-rithms that incorporate these methods.The knowledge discovery goals are defined by the intended use of the system. We can distinguish two types of goals: (1) verification and (2) discovery. With verification,the sys-tem is limited to verifying the user’s hypothe-sis. With discovery,the system autonomously finds new patterns. We further subdivide the discovery goal into prediction,where the sys-tem finds patterns for predicting the future behavior of some entities, and description, where the system finds patterns for presenta-tion to a user in a human-understandableform. In this article, we are primarily con-cerned with discovery-oriented data mining.Data mining involves fitting models to, or determining patterns from, observed data. The fitted models play the role of inferred knowledge: Whether the models reflect useful or interesting knowledge is part of the over-all, interactive KDD process where subjective human judgment is typically required. Two primary mathematical formalisms are used in model fitting: (1) statistical and (2) logical. The statistical approach allows for nondeter-ministic effects in the model, whereas a logi-cal model is purely deterministic. We focus primarily on the statistical approach to data mining, which tends to be the most widely used basis for practical data-mining applica-tions given the typical presence of uncertain-ty in real-world data-generating processes.Most data-mining methods are based on tried and tested techniques from machine learning, pattern recognition, and statistics: classification, clustering, regression, and so on. The array of different algorithms under each of these headings can often be bewilder-ing to both the novice and the experienced data analyst. It should be emphasized that of the many data-mining methods advertised in the literature, there are really only a few fun-damental techniques. The actual underlying model representation being used by a particu-lar method typically comes from a composi-tion of a small number of well-known op-tions: polynomials, splines, kernel and basis functions, threshold-Boolean functions, and so on. Thus, algorithms tend to differ primar-ily in the goodness-of-fit criterion used toevaluate model fit or in the search methodused to find a good fit.In our brief overview of data-mining meth-ods, we try in particular to convey the notionthat most (if not all) methods can be viewedas extensions or hybrids of a few basic tech-niques and principles. We first discuss the pri-mary methods of data mining and then showthat the data- mining methods can be viewedas consisting of three primary algorithmiccomponents: (1) model representation, (2)model evaluation, and (3) search. In the dis-cussion of KDD and data-mining methods,we use a simple example to make some of thenotions more concrete. Figure 2 shows a sim-ple two-dimensional artificial data set consist-ing of 23 cases. Each point on the graph rep-resents a person who has been given a loanby a particular bank at some time in the past.The horizontal axis represents the income ofthe person; the vertical axis represents the to-tal personal debt of the person (mortgage, carpayments, and so on). The data have beenclassified into two classes: (1) the x’s repre-sent persons who have defaulted on theirloans and (2) the o’s represent persons whoseloans are in good status with the bank. Thus,this simple artificial data set could represent ahistorical data set that can contain usefulknowledge from the point of view of thebank making the loans. Note that in actualKDD applications, there are typically manymore dimensions (as many as several hun-dreds) and many more data points (manythousands or even millions).ArticlesFALL 1996 43Figure 2. A Simple Data Set with Two Classes Used for Illustrative Purposes.。
采矿专业毕业设计外文翻译--复杂性科学及其在煤矿安全行为问题研究方面的启示
附录A复杂性科学及其在煤矿安全行为问题研究方面的启示摘要：复杂性科学是一种前沿的系统科学近年，中国的煤炭工业迅速发展，并为经济的增长作出了重要的贡献。
煤矿的安全问题对于煤炭工业的可持续发展来说历来是一个桎梏。
站在一个新的视角来看，煤矿安全问题的研究是一个值得重视的课题。
本文把近几年中复杂性科学的发展与其在煤矿安全行为问题研究方面的启示相结合，对其进行了系统的研究。
关键词：复杂性科学，矿井安全，混乱1复杂性科学的发展复杂性科学的研究对象为复杂的系统或系统的复杂性。
不同的学者对于复杂性给出了不同的定义。
但却难以给出一个精确的定义。
总结各种观点，系统的复杂性主要可以概括为以下几个方面：1 ) 系统各单元之间是一种广泛且紧密的网络式联系。
所以，其中任何一个单元的变化都可能影响到其他的单元并引起其他单元的变化。
2）系统是包含众多因素的一个多层次，多机能的结构。
每一个层次是一个单元，它有助于实现一个特定的机能。
3 ) 系统中包含有一个回馈循环。
在发展过程中，系统能不断的学习并改进其层次结构和机能结构。
4 ) 系统是开放的，它与外界有密切联系并相互作用。
5 ) 系统具有不断演变的自我适应能力。
6）它不能用常规的理论与方法来解释。
7 )系统的特性曲线是动态与非线性的。
8)系统具有跨越水平得特性，例如包含，关联，互相影响和作用。
复杂系统存在于我们世界的各个领域:物理系统,生态学系统,社会系统,等等.根据生命现象得的独立单元，智能信息结构，原料销售的起伏，社会的进步和衰退，人体的免疫系统，这些系统的共同特征是，在不确定的变化之后会出现一些不确定的秩序。
研究复杂性的目的不仅仅是揭示和描述其运转原理，而是要解决如何预测和处理复杂系统中那些以前无法解释和处理的问题。
在1984年，在诺贝尔物理学获奖人穆拉伊.耶尔-曼,腓力.安德森,和诺贝尔经济学的获奖人阿罗的赞助下,集合了一群来自物理学，经济学，生态学原理和计算机科学方面的研究专家，创建了著名的圣菲研究所。
抽象技术及其在蒙特卡洛树搜索中的应用研究综述
抽象技术及其在蒙特卡洛树搜索中的应用研究综述抽象技术及其在蒙特卡洛树搜索中的应用研究综述1. 引言随着计算机技术的发展，人工智能领域取得了长足的进步。
蒙特卡洛树搜索（Monte Carlo Tree Search，MCTS）作为一种强大的人工智能算法，广泛应用于各个领域，例如围棋、象棋、扑克等。
在MCTS算法的研究中，抽象技术起到了重要的作用。
本文旨在对抽象技术及其在MCTS中的应用进行综述，以期为深入理解MCTS算法的原理和应用提供参考。
2. 抽象技术概述抽象技术是通过将研究对象的某些细节或特征进行忽略，从而得到问题的简化描述或近似解的方法。
在蒙特卡洛树搜索中，抽象技术可以通过简化棋盘状态、减少搜索空间等方式来加快搜索速度，同时保持原问题的关键特征。
常用的抽象技术包括状态抽象、行动抽象和信息抽象。
3. 抽象技术在MCTS中的应用3.1 状态抽象状态抽象是指将复杂的游戏状态转化为简化的形式。
在MCTS 中，状态抽象可以用于减少搜索空间、去除冗余信息、降低计算复杂度等。
例如，在围棋中，可以将某些无关紧要的局面中的棋子合并为一个超级棋子。
这样可以大大减少搜索空间，提高MCTS的效率。
3.2 行动抽象行动抽象是指将具体的行动转化为抽象的行动。
在MCTS中，行动抽象可以用于减少搜索空间、合并相似的行动等。
例如，在象棋中，可以将某些相似的走法合并为一个抽象的走法，从而减少搜索的复杂度。
3.3 信息抽象信息抽象是指将游戏中的信息进行简化。
在MCTS中，信息抽象可以用于减少状态的表示维度、降低计算复杂度等。
例如，在扑克游戏中，可以将某些玩家的手牌信息进行抽象，只保留关键的信息，以减少搜索空间。
4. 抽象技术在不同领域的应用抽象技术在MCTS算法研究中有广泛的应用。
不同的领域可以利用不同的抽象技术进行问题简化。
以围棋为例，基于状态抽象的MCTS算法被广泛应用于AlphaGo等人工智能项目中。
通过精妙的状态抽象，AlphaGo在围棋领域取得了突破性的成果。
戈特弗里德威廉 莱布尼茨
谢谢观看
莱布尼茨的父亲是莱比锡大学的伦理学教授，在莱布尼茨6岁时去世，留下了一个私人的图书馆。12岁时自 学拉丁文，并着手学习希腊文。14岁时进入莱比锡大学念书。20岁时完成学业，专攻法律和一般大学课程。 1666年他出版第一部有关于哲学方面的书籍，书名为《论组合术》（de arte combinatoria）。
莱布尼茨的演算推论器，很能让人想起符号逻辑，可以被看作使这种计算成为可行的一种方式。莱布尼茨写 的备忘录（帕金森1966年翻译了它们）可以被看作是对符号逻辑的探索——所以他的演算——上路了。但是 Gerhard和Couturat没有出版这些著作，直到现代形式逻辑在19世纪80年代于Frege的概念文字和Charles P e i r c e 及 他 的 学 生 的 著 作 中 形 成 ， 所 以 就 更 在 乔 治 ·布 尔 和 德 ·摩 根 在 1 8 4 7 开 创 这 种 逻 辑 之 后 了 。
人物轶事
人物轶事
莱布尼茨与中国文化
1701年白晋给莱布尼茨的周易图 莱布尼茨是最早接触中华文化的欧洲人之一，从一些曾经前往中国传教的 教士那里接触到中国文化，之前应该从马可·波罗引起的东方热留下的影响中也了解过中国文化。法国汉学大师若 阿基姆·布韦（Joachim Bouvet，汉名白晋，1662－1732年）向莱布尼茨介绍了《周易》和八卦的系统。在莱 布尼茨眼中，“阴”与“阳”基本上就是他的二进制的中国版。他曾断言：“二进制乃是具有世界普遍性的、最 完美的逻辑语言”。如今在德国图林根，著名的郭塔王宫图书馆（Schlossbibliothek zu Gotha）内仍保存一份 莱氏的手稿，标题写着“1与0，一切数字的神奇渊源。”
淋巴管内皮透明质酸受体-1在乳腺癌中的表达及意义
淋巴管内皮透明质酸受体-1在乳腺癌中的表达及意义郑飞;和钢【摘要】目的探讨淋巴管内皮透明质酸受体-1(LYVE-1)在乳腺癌组织中的表达及临床意义.方法采用免疫组化方法对70例乳腺癌组织和20例乳腺纤维腺瘤组织中的LYVE-1表达进行检测,计算淋巴管密度(LVD),分析LYVE-1的表达水平与乳腺癌淋巴结转移及其他病理参数[年龄、肿瘤大小、病理分期、雌激素受体(ER)、孕激素受体(PR)及C-erbB2]间的关系.结果 LYVE-1所标记的乳腺癌组织中LVD明显高于转移者(P<0.01).乳腺癌患者不同年龄、肿瘤大小、ER、PR及C-erbB2 LVD 比较差异无统计学意义(P>0.05),但不同病理分期患者LVD比较差异有统计学意义(P<0.01).结论 LYVE-1的表达与乳腺癌的淋巴道转移有关,LVD可作为乳腺癌淋巴道转移的生物学指标,对乳腺癌临床预后的评估有一定的指导意义.【期刊名称】《现代实用医学》【年(卷),期】2010(022)002【总页数】3页(P139-141)【关键词】乳腺肿瘤;癌;淋巴管内皮透明质酸受体-1【作者】郑飞;和钢【作者单位】宁波大学医学院,浙江宁波,315211;宁波市第二医院,浙江宁波,315010【正文语种】中文【中图分类】R737.9乳腺癌的淋巴转移发生较早，同时又是决定乳腺癌分期和选择治疗方案最关键的因素之一。
长期以来由于缺乏淋巴管内皮细胞特异性标志物，因而对肿瘤的淋巴管生成研究一直滞后于肿瘤血管生成的研究。
近年来，新近发现的淋巴管内皮透明质酸受体-1（LYVE-1）被认为是目前特异性最强的淋巴管内皮细胞标志物之一，尤为引人注目。
本研究采用免疫组化方法检测 LYVE-1在乳腺肿瘤中的表达，来探讨淋巴管生成与乳腺癌转移的关系。
报告如下。
1 材料和方法1.1 材料收集宁波市第二医院肿瘤外科2006年1月至2007年10月，均经手术切除并病理证实为原发性乳腺癌的石蜡包埋标本70例，术前均未行放、化疗及内分泌治疗。
媒介专业名词解释
媒介专业名词解释中文英文释义推及人口Universe 特定目标受众人口的多少，是计算媒体到达率，收视率及成本效益的基数。
例如：15-34年岁居住在上海的男性人口为58万，在上海东方台新闻报道一个广告可达5个TARP，那么，接触的人数为29000。
目标受众Target Audience 广告想要传达到的那一群人。
只有对目标受众的深入了解，才能作出有效的媒介计划和购买收视率/收听率Ratings 某一媒体在特定时间之内的覆盖的个人或家庭在总数所占的百分比一个收视点等于某类对象的一个百分点总收视点GRP-Gross RatingPoint在电视播出广告量的量度单位，可把不同时段，不同日子的收视点累加在一起，然后作比较，有重叠性，总收视点(GRP) ＝到达率(R) X 接触频率(F)目标受众总收视点TARPs-TargetAudience RatingPoints和GRP的概念相同，只是针对目标受众的收视点累加。
到达率Reach 广告位每日接触人次占总人口的比率。
计算方法：每日实际接触到广告的人口/该地区的总人口×100%净到达率Net Reach 有机会视/听广告一次或以上的人口百分比，不包括重叠部分，或称净到达率，最极限只能达100％有效到达率Effective Reach 有机会视/听广告达一定次数的目标受众百分比，一般以到达3次以上计算接触频率Frequency 一特定时期内，有机会收看到某个广告的次数平均接触频率Ave Frequency 一特定时期内，平均有机会收看到某个广告的次数，广告频次及到达率在户外广告来讲通常以四周以上为一个衡量时段。
平均接触频次= 总收视点/净到达率受众分散度Spread ratio 指一天/一周内媒体受众接触各类媒体的多少每收视点成本CPRP 评估同一地区不同电视频道或不同时段的价钱每个收视点的价格CPRP＝媒体价格/ 收视点每千人成本CPM（cost per 评估接触每一千个目标受众的价格或由某一媒介或媒介广告排期表所送达1000thousand）人所需的成本CPM=媒体价格/ 接触人次/ 1000 OTS Opportunity To See有机会看到广告的人群，可重复计算持续式媒体排期Continuous Schedule 广告在整个活动期间持续发布，没有什么变动。
基于深度学习的岩石薄片矿物自动识别方法
基于深度学习的岩石薄片矿物自动识别方法，一般可以分为以下几个步骤：
数据准备：需要收集大量的岩石薄片图像数据，并进行标注，将每张图像对应的矿物种类标注出来。
模型选择：选择适合岩石薄片矿物自动识别的深度学习模型，例如卷积神经网络（CNN）模型、残差网络（ResNet）模型等。
数据预处理：对收集的岩石薄片图像数据进行预处理，例如图像缩放、数据增强等，以便模型更好地学习和识别。
模型训练：使用已标注的岩石薄片图像数据进行模型训练，通过反向传播算法不断优化模型的参数，以提高模型的准确率。
模型评估：使用一部分未在训练集中出现过的岩石薄片图像数据对训练好的模型进行评估，计算模型的准确率、召回率、F1值等指标，以判断模型的表现如何。
模型部署：将训练好的模型部署到实际应用场景中，实现岩石薄片矿物自动识别的功能。
需要注意的是，深度学习模型需要大量的数据和计算资源来训练，因此在实际应用中需要考虑数据和计算资源的可用性。
同时，在数据准备和模型训练过程中也需要对数据进行合理的采样和划分，以保证模型的泛化能力和稳定性。
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People RecordID Age Married NumCars 100 23 No 1 200 25 Yes 1 300 29 No 0 400 34 Yes 2 500 38 Yes 2 minimum support = 40, minimum con dence = 50 Rules Sample Support Con dence hAge: 30..39i and hMarried: Yesi hNumCars: 2i 40 100 hNumCars: 0..1i hMarried: Noi 40 66.6 Figure 1: Example of Quantitative Association Rules quantitative association rules. Figure 2 shows this mapping for the non-key attributes of the People table given in Figure 1. Age is partitioned into two intervals: 20..29 and 30..39. The categorical attribute, Married, has two boolean attributes Married: Yes" and Married: No". Since the number of values for NumCars is small, NumCars is not partitioned into intervals; each value is mapped to a boolean eld. Record 100, which had hAge: 23i now has Age: 20..29" equal to 1", Age: 30..39" equal to 0", etc.
Also, Department of Computer Science, University of Wisconsin, Madison.
table has an attribute corresponding to each item and a record corresponding to each transaction. The value of an attribute for a given record is 1" if the item corresponding to the attribute is present in the transaction corresponding to the record, 0" else. In the rest of the paper, we refer to this problem as the Boolean Association Rules problem. Relational tables in most business and scienti c domains have richer attribute types. Attributes can be quantitative e.g. age, income or categorical e.g. zip code, make of car. Boolean attributes can be considered a special case of categorical attributes. In this paper, we de ne the problem of mining association rules over quantitative and categorical attributes in large relational tables and present techniques for discovering such rules. We refer to this mining problem as the Quantitative Association Rules problem. We give a formal statement of the problem in Section 2. For illustration, Figure 1 shows a People table with three non-key attributes. Age and NumCars are quantitative attributes, whereas Married is a categorical attribute. A quantitative association rule present in this table is: hAge: 30..39i and hMarried: Yesi hNumCars: 2i.
Mining Quantitative Association Rules in Large Relational Tables
Ramakrishnan Srikant
IBM Almaden Research Center San Jose, CA 95120 IBM Almaden Research Center San Jose, CA 95120
1.1 Mapping the Quantitative Association Rules Problem into the Boolean Association Rules Problem
Let us examine whether the Quantitative Association Rules problem can be mapped to the Boolean Association Rules problem. If all attributes are categorical or the quantitative attributes have only a few values, this mapping is straightforward. Conceptually, instead of having just one eld in the table for each attribute, we have as many elds as the number of attribute values. The value of a boolean eld corresponding to hattribute1, value1i would be 1" if attribute1 had value1 in the original record, and 0" otherwise. If the domain of values for a quantitative approach is large, an obvious approach will be to rst partition the values into intervals and then map each hattribute, intervali pair to a boolean attribute. We can now use any algorithm for nding Boolean Association Rules e.g. AS94 to nd 1
Breaking the logjam. To break the above catch-22
Mapping Woes. There are two problems with this
AbstractLeabharlann 1 Introduction
Data mining, also known as knowledge discovery in databases, has been recognized as a new area for database research. The problem of discovering association rules was introduced in AIS93 . Given a set of transactions, where each transaction is a set of items, an association rule is an expression of the from X Y , where X and Y are sets of items. An example of an association rule is: 30 of transactions that contain beer also contain diapers; 2 of all transactions contain both of these items". Here 30 is called the con dence of the rule, and 2 the support of the rule. The problem is to nd all association rules that satisfy user-speci ed minimum support and minimum con dence constraints. Conceptually, this problem can be viewed as nding associations between the 1" values in a relational table where all the attributes are boolean. The
Rakesh Agrawal
We introduce the problem of mining association rules in large relational tables containing both quantitative and categorical attributes. An example of such an association might be 10 of married people between age 50 and 60 have at least 2 cars". We deal with quantitative attributes by nepartitioning the values of the attribute and then combining adjacent partitions as necessary. We introduce measures of partial completeness which quantify the information lost due to partitioning. A direct application of this technique can generate too many similar rules. We tackle this problem by using a greater-than-expected-value" interest measure to identify the interesting rules in the output. We give an algorithm for mining such quantitative association rules. Finally, we describe the results of using this approach on a real-life dataset.