数据挖掘导论(英文版)
暫譯: 數據挖掘導論（英文版）

本書全面介紹了數據挖掘（Data Mining）的理論和方法，著重介紹如何用數據挖掘知識解決各種實際問題，涉及學科領域眾多，適用面廣。書中涵蓋五個主題︰數據（Data）、分類（Classification）、關聯分析（Association Analysis）、聚類（Clustering）和異常檢測（Anomaly Detection）。除異常檢測外，每個主題都包含兩章︰前面一章講述基本概念、代表性算法和評估技術，後面一章較深入地討論高級概念和算法。目的是使讀者在透徹地理解數據挖掘基礎的同時，還能了解更多重要的高級主題。包含大量的圖表、綜合示例和豐富的習題。‧不需要數據庫背景。只需要很少的統計學或數學背景知識。‧網上配套教輔資源豐富，包括PPT、習題解答、數據集等。

Preface 
1 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 Exercises 
2 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 Exercises 
3 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 Exercises 
5 Classification: Alternative Techniques 
6 Association Analysis: Basic Concepts and Algorithms 
7 Association Analysis:Advanced Concepts 
8 Cluster Analysis:Basic Concepts and Algorithms 
9 Cluster Analysis:Additional Issues and Algorithms 
10 Anomaly Detection 
Appendix A Linear Algebra 
Appendix B Dimensionality Reduction 
Appendix C Probability and Statistics 
Appendix D Regression 
Appendix E Optimization 
Author Index 
Subject Index 
Copyright Permissions

Preface 

1 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 Exercises 

2 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 Exercises 

3 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 Exercises 

5 Classification: Alternative Techniques 

6 Association Analysis: Basic Concepts and Algorithms 

7 Association Analysis:Advanced Concepts 

8 Cluster Analysis:Basic Concepts and Algorithms 

9 Cluster Analysis:Additional Issues and Algorithms 

10 Anomaly Detection 

Appendix A Linear Algebra 

Appendix B Dimensionality Reduction 

Appendix C Probability and Statistics 

Appendix D Regression 

Appendix E Optimization 

Author Index 

Subject Index 

Copyright Permissions