Front cover -- Title page -- Copyright page -- Dedication -- Table of contents -- Foreword -- Preface -- Organization of the Book -- To the Instructor -- To the Student -- To the Professional -- Book Websites with Resources -- Acknowledgments for the First Edition of the Book -- Acknowledgments for the Second Edition of the Book -- 1 Introduction -- 1.1 What Motivated Data Mining? Why Is It Important? -- 1.2 So, What Is Data Mining? -- 1.3 Data Mining-On What Kind of Data? -- 1.4 Data Mining Functionalities-What Kinds of Patterns Can Be Mined? -- 1.5 Are All of the Patterns Interesting? -- 1.6 Classification of Data Mining Systems -- 1.7 Data Mining Task Primitives -- 1.8 Integration of a Data Mining System with a Database or Data Warehouse System -- 1.9 Major Issues in Data Mining -- 1.10 Summary -- Exercises -- Bibliographic Notes -- 2 Data Preprocessing -- 2.1 Why Preprocess the Data? -- 2.2 Descriptive Data Summarization -- 2.3 Data Cleaning -- 2.4 Data Integration and Transformation -- 2.5 Data Reduction -- 2.6 Data Discretization and Concept Hierarchy Generation -- 2.7 Summary -- Exercises -- Bibliographic Notes -- 3 Data Warehouse and OLAP Technology: An Overview -- 3.1 What Is a Data Warehouse? -- 3.2 A Multidimensional Data Model -- 3.3 Data Warehouse Architecture -- 3.4 Data Warehouse Implementation -- 3.5 From Data Warehousing to Data Mining -- 3.6 Summary -- Exercises -- Bibliographic Notes -- 4 Data Cube Computation and Data Generalization -- 4.1 Efficient Methods for Data Cube Computation -- 4.2 Further Development of Data Cube and OLAP Technology -- 4.3 Attribute-Oriented Induction-An Alternative Method for Data Generalization and Concept Description -- 4.4 Summary -- Exercises -- Bibliographic Notes -- 5 Mining Frequent Patterns, Associations, and Correlations -- 5.1 Basic Concepts and a Road Map.

5.2 Efficient and Scalable Frequent Itemset Mining Methods -- 5.3 Mining Various Kinds of Association Rules -- 5.4 From Association Mining to Correlation Analysis -- 5.5 Constraint-Based Association Mining -- 5.6 Summary -- Exercises -- Bibliographic Notes -- 6 Classification and Prediction -- 6.1 What Is Classification? What Is Prediction? -- 6.2 Issues Regarding Classification and Prediction -- 6.3 Classification by Decision Tree Induction -- 6.4 Bayesian Classification -- 6.5 Rule-Based Classification -- 6.6 Classification by Backpropagation -- 6.7 Support Vector Machines -- 6.8 Associative Classification: Classification by Association Rule Analysis -- 6.9 Lazy Learners (or Learning from Your Neighbors) -- 6.10 Other Classification Methods -- 6.11 Prediction -- 6.12 Accuracy and Error Measures -- 6.13 Evaluating the Accuracy of a Classifier or Predictor -- 6.14 Ensemble Methods-Increasing the Accuracy -- 6.15 Model Selection -- 6.16 Summary -- Exercises -- Bibliographic Notes -- 7 Cluster Analysis -- 7.1 What Is Cluster Analysis? -- 7.2 Types of Data in Cluster Analysis -- 7.3 A Categorization of Major Clustering Methods -- 7.4 Partitioning Methods -- 7.5 Hierarchical Methods -- 7.6 Density-Based Methods -- 7.7 Grid-Based Methods -- 7.8 Model-Based Clustering Methods -- 7.9 Clustering High-Dimensional Data -- 7.10 Constraint-Based Cluster Analysis -- 7.11 Outlier Analysis -- 7.12 Summary -- Exercises -- Bibliographic Notes -- 8 Mining Stream, Time-Series, and Sequence Data -- 8.1 Mining Data Streams -- 8.2 Mining Time-Series Data -- 8.3 Mining Sequence Patterns in Transactional Databases -- 8.4 Mining Sequence Patterns in Biological Data -- 8.5 Summary -- Exercises -- Bibliographic Notes -- 9 Graph Mining, Social Network Analysis, and Multirelational Data Mining -- 9.1 Graph Mining -- 9.2 Social Network Analysis -- 9.3 Multirelational Data Mining.

9.4 Summary -- Exercises -- Bibliographic Notes -- 10 Mining Object, Spatial, Multimedia, Text, and Web Data -- 10.1 Multidimensional Analysis and Descriptive Mining of Complex Data Objects -- 10.2 Spatial Data Mining -- 10.3 Multimedia Data Mining -- 10.4 Text Mining -- 10.5 Mining the World Wide Web -- 10.6 Summary -- Exercises -- Bibliographic Notes -- 11 Applications and Trends in Data Mining -- 11.1 Data Mining Applications -- 11.2 Data Mining System Products and Research Prototypes -- 11.3 Additional Themes on Data Mining -- 11.4 Social Impacts of Data Mining -- 11.5 Trends in Data Mining -- 11.6 Summary -- Exercises -- Bibliographic Notes -- Appendix: An Introduction to Microsoft's OLE DB for Data Mining -- A.1 Model Creation -- A.2 Model Training -- A.3 Model Prediction and Browsing -- Bibliography.