| 本书是明尼苏达大学和密歇根州立大学数据挖掘课程的教材,该书全面介绍了数据挖掘，涵盖了五个主题：数据、分类、关联分析、聚类和异常检测。除异常检测外，每个主题都有两章：前一章涵盖基本概念、代表性算法和评估技术，而后一章讨论高级概念和算法。这样读者在透彻地理解数据挖掘的基础的同时,还能够了解更多重要的高级主题。本书适合作为相关专业高年级本科生和研究生数据挖掘课程的教材，同时也可作为从事数据挖掘研究和应用开发工作的技术人员的参考书。 |
| 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 4 Classification: Basic Concepts, Decision Trees, and Model Evaluation 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 |
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