数据挖掘导论
2010.9
机械工业出版社
(美)Pang-Ning Tan,Michael Steinbach,Vipin Kumar
769
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Advances in data generation and collection are producing data sets of massire size in commerce and a variety of scientific disciplines．Data warehouses store details of the sales and operations of businesses，Earth-orbiting satelfites beam high-resolution images and sensor data back to Earth．and genomics ex- periments generate sequence，structural，and functional data for an increasing number of organisms．The ease with Which data can now be gathered and stored has created a new attitude toward data analysis：Gather whatever data you can whenever and wherever possible．It has become an article of faith that the gathered data will have value．either for the purpose that initially motivated its collection or for purposes not yet envisioned．The field of data mining grew out of the limitations of current data analysis techniques in handling the challenges posed by these new types of data sets．Data mining does not replace other areas of data analysis，but rat．Her takes them as the foundation for much of its work．While some areas of data mining，such as association analysis，are unique to the field，other areas，such as clustering，classification, and anomaly detection，build upon a long history of work on these topics in other fields．Indeed．the willingness of data mining researchers to draw upon existing techniques has contributed to the strength and breadth of the field，as well as to its rapid growth．
本书全面介绍了数据挖掘的理论和方法，着重介绍如何用数据挖掘知识解决各种实际问题，涉及学科领域众多，适用面广。书中涵盖5个主题：数据、分类、关联分析、聚类和异常检测。除异常检测外，每个主题都包含两章：前面一章讲述基本概念、代表性算法和评估技术，后面一章较深入地讨论高级概念和算法。目的是使读者在透彻地理解数据挖掘基础的同时，还能了解更多重要的高级主题。.包含大量的图表、综合示例和丰富的习题。·不需要数据库背景。只需要很少的统计学或数学背景知识。·网上配套教辅资源丰富，包括PPT、习题解答、数据集等。
作者：（美国）谭（Pang-Ning Tan） （美国）斯坦巴克（Michael Steinbach） （美国）库马尔（Vipin Kumar）Pang.Ning Tan现为密歇根州立大学计算机与工程系助理教授，主要教授数据挖掘、数据库系统等课程。他的研究主要关注于为广泛的应用（包括医学信息学、地球科学、社会网络、Web挖掘和计算机安全）开发适用的数据挖掘算法。Michael Steinbach拥有明尼苏达大学数学学士学位、统计学硕士学位和计算机科学博士学位，现为明尼苏达大学双城分校计算机科学与工程系助理研究员。Vipin Kumar现为明尼苏达大学计算机科学与工程系主任和William Norris教授。1 988年至2005年。他曾担任美国陆军高性能计算研究中心主任。
Preface1 Introduction 1.1 What Is Data Mining? 1.2 Motivating Challenges 1.3 The Origins of Data Mining 1.4 Data Mining Tasks 1.5 Scope and Organization of the Book 1.6 Bibliographic Notes 1.7 Exercises2 Data 2.1 Types of Data 2.1.1 Attributes and Measurement 2.1.2 Types of Data Sets 2.2 Data Quality 2.2.1 Measurement and Data Collection Issues 2.2.2 Issues Related to Applications 2.3 Data Preprocessing 2.3.1 Aggregation 2.3.2 Sampling 2.3.3 Dimensionality Reduction 2.3.4 Feature Subset Selection 2.3.5 Feature Creation 2.3.6 Discretization and Binarization 2.3.7 Variable Transformation 2.4 Measures of Similarity and Dissimilarity 2.4.1 Basics 2.4.2 Similarity and Dissimilarity between Simple Attributes. 2.4.3 Dissimilarities between Data Objects 2.4.4 Similarities between Data Objects 2.4.5 Examples of Proximity Measures 2.4.6 Issues in Proximity Calculation 2.4.7 Selecting the Right Proximity Measure 2.5 Bibliographic Notes 2.6 Exercises3 Exploring Data 3.1 The Iris Data Set 3.2 Summary Statistics 3.2.1 Frequencies and the Mode 3.2.2 Percentiles 3.2.3 Measures of Location: Mean and Median 3.2.4 Measures of Spread: Range and Variance 3.2.5 Multivariate Summary Statistics 3.2.6 Other Ways to Summarize the Data 3.3 Visualization 3.3.1 Motivations for Visualization 3.3.2 General Concepts 3.3.3 Techniques 3.3.4 Visualizing Higher-Dimensional Data 3.3.5 Do's and Don'ts 3.4 OLAP and Multidimensional Data Analysis 3.4.1 Representing Iris Data as a Multidimensional Array 3.4.2 Multidimensional Data: The General Case 3.4.3 Analyzing Multidimensional Data 3.4.4 Final Comments on Multidimensional Data Analysis 3.5 Bibliographic Notes 3.6 Exercises Classification:4 Basic Concepts, Decision Trees, and Model Evaluation 4.1 Preliminaries 4.2 General Approach to Solving a Classification Problem 4.3 Decision Tree Induction 4.3.1 How a Decision Tree Works 4.3.2 How to Build a Decision Tree 4.3.3 Methods for Expressing Attribute Test Conditions . 4.3.4 Measures for Selecting the Best Split 4.3.5 Algorithm for Decision Tree Induction 4.3.6 An Example: Web Robot Detection 4.3.7 Characteristics of Decision Tree Induction 4.4 Model Overfitting 4.4.1 Overfitting Due to Presence of Noise 4.4.2 Overfitting Due to Lack of Representative Samples . 4.4.3 Overfitting and the Multiple Comparison Procedure 4.4.4 Estimation of Generalization Errors 4.4.5 Handling Overfitting in Decision Tree Induction . . 4.5 Evaluating the Performance of a Classifier 4.5.1 Holdout Method 4.5.2 Random Subsampling 4.5.3 Cross-Validation 4.5.4 Bootstrap 4.6 Methods for Comparing Classifiers 4.6.1 Estimating a Confidence Interval for Accuracy 4.6.2 Comparing the Performance of Two Models 4.6.3 Comparing the Performance of Two Classifiers 4.7 Bibliographic Notes 4.8 Exercises5 Classification: Alternative Techniques6 Association Analysis: Basic Concepts and Algorithms
插图：What Is an attribute?We start with a more detailed definition of an attribute.Definition 2.1. An attribute is a property or characteristic of an object that may vary, either from one object to another or from one time to another.For example, eye color varies from person to person, while the temperature of an object varies over time. Note that eye color is a symbolic attribute with a small number of possible values brown, black, blue, green, hazel, etc.}, while temperature is a numerical attribute with a potentially unlimited number of values.At the most basic level, attributes are not about numbers or symbols. However, to discuss and more precisely analyze the characteristics of objects, we assign numbers or symbols to them. To do this in a well-defined way, we need a measurement scale. Definition 2.2. A measurement scale is a rule (function) that associates a numerical or symbolic value with an attribute of an object.Formally, the process of measurement is the application of a measurement scale to associate a value with a particular attribute of a specific object. While this may seem a bit abstract, we engage in the process of measurement all the time. For instance, we step on a bathroom scale to determine our weight, we classify someone as male or female, or we count the number of chairs in a room to see if there will be enough to seat all the people coming to a meeting. In all these cases, the "physical value" of an attribute of an object is mapped to a numerical or symbolic value.With this background, we can now discuss the type of an attribute, a concept that is important in determining if a particular data analysis technique is consistent with a specific type of attribute.
《数据挖掘导论(英文版)》是经典原版书库。
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刚拿到书，翻开书，全是英文，表示很有挑战性，没办法，既然选择了远方，那就数据挖掘技术和英语水平一起提升吧，相信自己一定可以的
如果学习数据挖掘，建议从这本书开始，不建议看国内的书籍。
数据挖掘入门必备书籍！
内容全面，概念清晰，虽是导论，亦有足够的广度，以之入门甚好
读书必读经典，英文版的，刚拿手里，纸张还好，不过内容挺充实的
建议多看看原版书，可以理解作者原意。
英文的书，就是比翻译的好多了
强烈推荐这套书，中英文的都不错
刚买不久，学习中。据说不错。
读了一部分了，要继续读
nice stuff~
英文不错，既可以提高英语水平，还可以扩展专业词汇量
给女儿买的，她说:与原版本相同，质量不错。
经典教材，百读不厌
经典书籍，价格实在
浪费钱可耻啊，推荐买中文的
内容没得说，印刷质量也很好，推荐！
我真的不喜欢国外教材讲半天讲不到重点的感觉。可能是智商太低吧。无法理解这种书籍的美感。当然有些国外教材还是很不错的。
这本书用词浅显，介绍了数据挖掘的一般的技术，难度不大，要深入研究的话要阅读里面列出的论文材料了。
……不过真是不爽头两章半天讲不到重点的感觉，注意力一下子就涣散了啊！
数据挖掘导论【英文版】，边学技术边学英语
早有耳闻这本书，豆瓣评价不错就买了，作为数据挖掘入门再好不过了，英文版看起来要比那写劣质翻译版舒服多了，纸张有点薄，但是感觉还行。
数据挖掘的经典好书,价钱也挺实惠
数据挖掘的经典入门书籍 感觉很好
32开的
最好的数据挖掘书，可是纸张差的不行
英文版，慢慢看
小巧，全英文
适用面广
精美的书本，经典教材
数据导论学习必学~！
不错，帮朋友买的，暂时还没发现什么缺点。
便宜点的多点