数据挖掘导论（英文版·原书第2版）

本书从算法的角度介绍数据挖掘所使用的主要原理与技术。为了更好地理解数据挖掘技术如何用于各种类型的数据，研究这些原理与技术是至关重要的。 本书所涵盖的主题包括：数据预处理、预测建模、关联分析、聚类分析、异常检测和避免错误发现。通过介绍每个主题的基本概念和算法，为读者提供将数据挖掘应用于实际问题所需的必要背景以及使用方法。

Contents 1 Introduction 1 1.1 What Is Data Mining? 4 1.2 Motivating Challenges 5 1.3 The Origins of Data Mining 7 1.4 Data Mining Tasks 9 1.5 Scope and Organization of the Book 13 1.6 Bibliographic Notes 15 1.7 Exercises 21 2 Data 23 2.1 Types of Data 26 2.1.1 Attributes and Measurement 27 2.1.2 Types of Data Sets 34 2.2 Data Quality 42 2.2.1 Measurement and Data Collection Issues 42 2.2.2 Issues Related to Applications 49 2.3 Data Preprocessing 50 2.3.1 Aggregation 51 2.3.2 Sampling 52 2.3.3 Dimensionality Reduction 56 2.3.4 Feature Subset Selection 58 2.3.5 Feature Creation 61 2.3.6 Discretization and Binarization 63 2.3.7 Variable Transformation 69 2.4 Measures of Similarity and Dissimilarity 71 2.4.1 Basics 72 2.4.2 Similarity and Dissimilarity between Simple Attributes . 74 2.4.3 Dissimilarities between Data Objects 76 2.4.4 Similarities between Data Objects 78 2.4.5 Examples of Proximity Measures 79 2.4.6 Mutual Information 88 2.4.7 Kernel Functions* 90 2.4.8 Bregman Divergence* 94 2.4.9 Issues in Proximity Calculation 96 2.4.10 Selecting the Right Proximity Measure 98 2.5 Bibliographic Notes 100 2.6 Exercises 105 3 Classiftcation: Basic Concepts and Techniques 113 3.1 Basic Concepts 114 3.2 General Framework for Classi?cation 117 3.3 Decision Tree Classi?er 119 3.3.1 A Basic Algorithm to Build a Decision Tree 121 3.3.2 Methods for Expressing Attribute Test Conditions 124 3.3.3 Measures for Selecting an Attribute Test Condition 127 3.3.4 Algorithm for Decision Tree Induction 136 3.3.5 Example Application: Web Robot Detection 138 3.3.6 Characteristics of Decision Tree Classi?ers 140 3.4 Model Over?tting 147 3.5 Model Selection 156 3.5.1 Using a Validation Set 156 3.5.2 Incorporating Model Complexity 157 3.5.3 Estimating Statistical Bounds 162 3.5.4 Model Selection for Decision Trees 162 3.6 Model Evaluation 164 3.6.1 Holdout Method 165 3.6.2 Cross-Validation 165 3.7 Presence of Hyper-parameters 168 3.7.1 Hyper-parameter Selection 168 3.7.2 Nested Cross-Validation 170 3.8 Pitfalls of Model Selection and Evaluation 172 3.8.1 Overlap between Training and Test Sets 172 3.8.2 Use of Validation Error as Generalization Error 172 3.9 Model Comparison? 173 3.9.1 Estimating the Con?dence Interval for Accuracy 174 3.9.2 Comparing the Performance of Two Models 175 3.10 Bibliographic Notes 176 3.11 Exercises 185 4 Classiftcation: Alternative Techniques 193 4.1 Types of Classi?ers 193 4.2 Rule-Based Classi?er 195 4.2.1 How a Rule-Based Classi?er Works 197 4.2.2 Properties of a Rule Set 198 4.2.3 Direct Methods for Rule Extraction 199 4.2.4 Indirect Methods for Rule Extraction 204 4.2.5 Characteristics of Rule-Based Classi?ers 206 4.3 Nearest Neighbor Classi?ers 208 4.3.1 Algorithm 209 4.3.2 Characteristics of Nearest Neighbor Classi?ers 210 4.4 Na¨ive Bayes Classi?er 212 4.4.1 Basics of Probability Theory 213 4.4.2 Na¨ive Bayes Assumption 218 4.5 Bayesian Networks 227 4.5.1 Graphical Representation 227 4.5.2 Inference and Learning 233 4.5.3 Characteristics of Bayesian Networks 242 4.6 Logistic Regression 243 4.6.1 Logistic Regression as a Generalized Linear Model 244 4.6.2 Learning Model Parameters 245 4.6.3 Characteristics of Logistic Regression 248 4.7 Arti?cial Neural Network (ANN) 249 4.7.1 Perceptron 250 4.7.2 Multi-layer Neural Network 254 4.7.3 Characteristics of ANN 261 4.8 Deep Learning 262 4.8.1 Using Synergistic Loss Functions 263 4.8.2 Using Responsive Activation Functions 266 4.8.3 Regularization 268 4.8.4 Initialization of Model Parameters 271 4.8.5 Characteristics of Deep Learning 275 4.9 Support Vector Machine (SVM) 276 4.9.1 Margin of a Separating Hyperplane 276 4.9.2 Linear SVM 278 4.9.3 Soft-margin SVM 284 4.9.4 Nonlinear SVM 290 4.9.5 Characteristics of SVM 294 4.10 Ensemble Methods 296 4.10.1 Rationale for Ensemble Method 297 4.10.2 Methods for Constructing an Ensemble Classi?er 297 4.10.3 Bias-Variance Decomposition 300 4.10.4 Bagging 302 4.10.5 Boosting 305 4.10.6 Random Forests 310 4.10.7 Empirical Comparison among Ensemble Methods 312 4.11 Class Imbalance Problem 313 4.11.1 Building Classi?ers with Class Imbalance 314 4.11.2 Evaluating Performance with Class Imbalance 318 4.11.3 Finding an Optimal Score Threshold 322 4.11.4 Aggregate Evaluation of Performance 323 4.12 Multiclass Problem 330 4.13 Bibliographic Notes 333 4.14 Exercises 345 5 Association Analysis: Basic Concepts and Algorithms 357 5.1 Preliminaries 358 5.2 Frequent Itemset Generation 362 5.2.1 The Apriori Principle 363 5.2.2 Frequent Itemset Generation in the Apriori Algorithm . 364 5.2.3 Candidate Generation and Pruning 368 5.2.4 Support Counting 373 5.2.5 Computational Complexity 377 5.3 Rule Generation 380 5.3.1 Con?dence-Based Pruning 380 5.3.2 Rule Generation in Apriori Algorithm 381 5.3.3 An Example: Congressional Voting Records 382 5.4 Compact Representation of Frequent Itemsets 384 5.4.1 Maximal Frequent Itemsets 384 5.4.2 Closed Itemsets 386 5.5 Alternative Methods for Generating Frequent Itemsets* 389 5.6 FP-Growth Algorithm* 393 5.6.1 FP-Tree Representation 394 5.6.2 Frequent Itemset Generation in FP-Growth Algorithm . 397 5.7 Evaluation of Association Patterns 401 5.7.1 Objective Measures of Interestingness 402 5.7.2 Measures beyond Pairs of Binary Variables 414 5.7.3 Simpson’s Paradox 416 5.8 E?ect of Skewed Support Distribution 418 5.9 Bibliographic Notes 424 5.10 Exercises 438 6 Association Analysis: Advanced Concepts 451 6.1 Handling Categorical Attributes 451 6.2 Handling Continuous Attributes 454 6.2.1 Discretization-Based Methods 454 6.2.2 Statistics-Based Methods 458 6.2.3 Non-discretization Methods 460 6.3 Handling a Concept Hierarchy 462 6.4 Sequential Patterns 464 6.4.1 Preliminaries 465 6.4.2 Sequential Pattern Discovery 468 6.4.3 Timing Constraints? 473 6.4.4 Alternative Counting Schemes? 477 6.5 Subgraph Patterns 479 6.5.1 Preliminaries 480 6.5.2 Frequent Subgraph Mining 483 6.5.3 Candidate Generation 487 6.5.4 Candidate Pruning 493 6.5.5 Support Counting 493 6.6 Infrequent Patterns? 493 6.6.1 Negative Patterns 494 6.6.2 Negatively Correlated Patterns 495 6.6.3 Comparisons among Infrequent Patterns, Negative Patterns, and Negatively Correlated Patterns 496 6.6.4 Techniques for Mining Interesting Infrequent Patterns . 498 6.6.5 Techniques Based on Mining Negative Patterns 499 6.6.6 Techniques Based on Support Expectation 501 6.7 Bibliographic Notes 505 6.8 Exercises 510 7 Cluster Analysis: Basic Concepts and Algorithms 525 7.1 Overview 528 7.1.1 What Is Cluster Analysis? 528 7.1.2 Di?erent Types of Clusterings 529 7.1.3 Di?erent Types of Clusters 531 7.2 K-means 534 7.2.1 The Basic K-means Algorithm 535 7.2.2 K-means: Additional Issues 544 7.2.3 Bisecting K-means 547 7.2.4 K-means and Di?erent Types of Clusters 548 7.2.5 Strengths and Weaknesses 549 7.2.6 K-means as an Optimization Problem 549 7.3 Agglomerative Hierarchical Clustering 554 7.3.1 Basic Agglomerative Hierarchical Clustering Algorithm 555 7.3.2 Speci?c Techniques 557 7.3.3 The Lance-Williams Formula for Cluster Proximity 562 7.3.4 Key Issues in Hierarchical Clustering 563 7.3.5 Outliers 564 7.3.6 Strengths and Weaknesses 565 7.4 DBSCAN 565 7.4.1 Traditional Density: Center-Based Approach 565 7.4.2 The DBSCAN Algorithm 567 7.4.3 Strengths and Weaknesses 569 7.5 Cluster Evaluation 571 7.5.1 Overview 571 7.5.2 Unsupervised Cluster Evaluation Using Cohesion and Separation 574 7.5.3 Unsupervised Cluster Evaluation Using the Proximity Matrix 582 7.5.4 Unsupervised Evaluation of Hierarchical Clustering 585 7.5.5 Determining the Correct Number of Clusters 587 7.5.6 Clustering Tendency 588 7.5.7 Supervised Measures of Cluster Validity 589 7.5.8 Assessing the Signi?cance of Cluster Validity Measures . 594 7.5.9 Choosing a Cluster Validity Measure 596 7.6 Bibliographic Notes 597 7.7 Exercises 603 8 Cluster Analysis: Additional Issues and Algorithms 613 8.1 Characteristics of Data, Clusters, and Clustering Algorithms . 614 8.1.1 Example: Comparing K-means and DBSCAN 614 8.1.2 Data Characteristics 615 8.1.3 Cluster Characteristics 617 8.1.4 General Characteristics of Clustering Algorithms 619 8.2 Prototype-Based Clustering 621 8.2.1 Fuzzy Clustering 621 8.2.2 Clustering Using Mixture Models 627 8.2.3 Self-Organizing Maps (SOM) 637 8.3 Density-Based Clustering 644 8.3.1 Grid-Based Clustering 644 8.3.2 Subspace Clustering 648 8.3.3 DENCLUE: A Kernel-Based Scheme for Density-Based Clustering 652 8.4 Graph-Based Clustering 656 8.4.1 Sparsi?cation 657 8.4.2 Minimum Spanning Tree (MST) Clustering 658 8.4.3 OPOSSUM: Optimal Partitioning of Sparse Similarities Using METIS 659 8.4.4 Chameleon: Hierarchical Clustering with Dynamic Modeling 660 8.4.5 Spectral Clustering 666 8.4.6 Shared Nearest Neighbor Similarity 673 8.4.7 The Jarvis-Patrick Clustering Algorithm 676 8.4.8 SNN Density 678 8.4.9 SNN Density-Based Clustering 679 8.5 Scalable Clustering Algorithms 681 8.5.1 Scalability: General Issues and Approaches 681 8.5.2 BIRCH 684 8.5.3 CURE 686 8.6 Which Clustering Algorithm? 690 8.7 Bibliographic Notes 693 8.8 Exercises 699 9 Anomaly Detection 703 9.1 Characteristics of Anomaly Detection Problems 705 9.1.1 A De?nition of an Anomaly 705 9.1.2 Nature of Data 706 9.1.3 How Anomaly Detection is Used 707 9.2 Characteristics of Anomaly Detection Methods 708 9.3 Statistical Approaches 710 9.3.1 Using Parametric Models 710 9.3.2 Using Non-parametric Models 714 9.3.3 Modeling Normal and Anomalous Classes 715 9.3.4 Assessing Statistical Signi?cance 717 9.3.5 Strengths and Weaknesses 718 9.4 Proximity-based Approaches 719 9.4.1 Distance-based Anomaly Score 719 9.4.2 Density-based Anomaly Score 720 9.4.3 Relative Density-based Anomaly Score 722 9.4.4 Strengths and Weaknesses 723 9.5 Clustering-based Approaches 724 9.5.1 Finding Anomalous Clusters 724 9.5.2 Finding Anomalous Instances 725 9.5.3 Strengths and Weaknesses 728 9.6 Reconstruction-based Approaches 728 9.7 One-class Classi?cation 732 9.7.1 Use of Kernels 733 9.7.2 The Origin Trick 734 9.7.3 Strengths and Weaknesses 738 9.8 Information Theoretic Approaches 738 9.9 Evaluation of Anomaly Detection 740 9.10 Bibliographic Notes 742 9.11 Exercises 749 10 Avoiding False Discoveries 755 10.1 Preliminaries: Statistical Testing 756 10.1.1 Signi?cance Testing 756 10.1.2 Hypothesis Testing 761 10.1.3 Multiple Hypothesis Testing 767 10.1.4 Pitfalls in Statistical Testing 776 10.2 Modeling Null and Alternative Distributions 778 10.2.1 Generating Synthetic Data Sets 781 10.2.2 Randomizing Class Labels 782 10.2.3 Resampling Instances 782 10.2.4 Modeling the Distribution of the Test Statistic 783 10.3 Statistical Testing for Classi?cation 783 10.3.1 Evaluating Classi?cation Performance 783 10.3.2 Binary Classi?cation as Multiple Hypothesis Testing 785 10.3.3 Multiple Hypothesis Testing in Model Selection 786 10.4 Statistical Testing for Association Analysis 787 10.4.1 Using Statistical Models 788 10.4.2 Using Randomization Methods 794 10.5 Statistical Testing for Cluster Analysis 795 10.5.1 Generating a Null Distribution for Internal Indices 796 10.5.2 Generating a Null Distribution for External Indices 798 10.5.3 Enrichment 798 10.6 Statistical Testing for Anomaly Detection 800 10.7 Bibliographic Notes 803 10.8 Exercises 808

[美]陈封能（Pang-Ning Tan）迈克尔·斯坦巴赫（Michael Steinbach）阿努吉·卡帕坦（Anuj Karpatne）维平·库玛尔（Vipin Kumar）著：陈封能（Pang-Ning Tan） 密歇根州立大学计算机科学与工程系教授，主要研究方向是数据挖掘、数据库系统、网络空间安全、网络分析等。

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  来源：豆瓣 发布时间：2025-05-17 04:29:01