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• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• Foreword
• Foreword to second edition
• Preface
• Organization of the book
• To the instructor
• To the student
• To the professional
• Book web site with resources
• Acknowledgments
• About the authors
• Chapter 1: Introduction
• 1.1. What is data mining?
• 1.2. Data mining: an essential step in knowledge discovery
• 1.3. Diversity of data types for data mining
• 1.4. Mining various kinds of knowledge
• 1.5. Data mining: confluence of multiple disciplines
• 1.6. Data mining and applications
• 1.7. Data mining and society
• 1.8. Summary
• 1.9. Exercises
• 1.10. Bibliographic notes
• Bibliography
• Chapter 2: Data, measurements, and data preprocessing
• 2.1. Data types
• 2.2. Statistics of data
• 2.3. Similarity and distance measures
• 2.4. Data quality, data cleaning, and data integration
• 2.5. Data transformation
• 2.6. Dimensionality reduction
• 2.7. Summary
• 2.8. Exercises
• 2.9. Bibliographic notes
• Bibliography
• Chapter 3: Data warehousing and online analytical processing
• 3.1. Data warehouse
• 3.2. Data warehouse modeling: schema and measures
• 3.3. OLAP operations
• 3.4. Data cube computation
• 3.5. Data cube computation methods
• 3.6. Summary
• 3.7. Exercises
• 3.8. Bibliographic notes
• Bibliography
• Chapter 4: Pattern mining: basic concepts and methods
• 4.1. Basic concepts
• 4.2. Frequent itemset mining methods
• 4.3. Which patterns are interesting?—Pattern evaluation methods
• 4.4. Summary
• 4.5. Exercises
• 4.6. Bibliographic notes
• Bibliography
• Chapter 5: Pattern mining: advanced methods
• 5.1. Mining various kinds of patterns
• 5.2. Mining compressed or approximate patterns
• 5.3. Constraint-based pattern mining
• 5.4. Mining sequential patterns
• 5.5. Mining subgraph patterns
• 5.6. Pattern mining: application examples
• 5.7. Summary
• 5.8. Exercises
• 5.9. Bibliographic notes
• Bibliography
• Chapter 6: Classification: basic concepts and methods
• 6.1. Basic concepts
• 6.2. Decision tree induction
• 6.3. Bayes classification methods
• 6.4. Lazy learners (or learning from your neighbors)
• 6.5. Linear classifiers
• 6.6. Model evaluation and selection
• 6.7. Techniques to improve classification accuracy
• 6.8. Summary
• 6.9. Exercises
• 6.10. Bibliographic notes
• Bibliography
• Chapter 7: Classification: advanced methods
• 7.1. Feature selection and engineering
• 7.2. Bayesian belief networks
• 7.3. Support vector machines
• 7.4. Rule-based and pattern-based classification
• 7.5. Classification with weak supervision
• 7.6. Classification with rich data type
• 7.7. Potpourri: other related techniques
• 7.8. Summary
• 7.9. Exercises
• 7.10. Bibliographic notes
• Bibliography
• Chapter 8: Cluster analysis: basic concepts and methods
• 8.1. Cluster analysis
• 8.2. Partitioning methods
• 8.3. Hierarchical methods
• 8.4. Density-based and grid-based methods
• 8.5. Evaluation of clustering
• 8.6. Summary
• 8.7. Exercises
• 8.8. Bibliographic notes
• Bibliography
• Chapter 9: Cluster analysis: advanced methods
• 9.1. Probabilistic model-based clustering
• 9.2. Clustering high-dimensional data
• 9.3. Biclustering
• 9.4. Dimensionality reduction for clustering
• 9.5. Clustering graph and network data
• 9.6. Semisupervised clustering
• 9.7. Summary
• 9.8. Exercises
• 9.9. Bibliographic notes
• Bibliography
• Chapter 10: Deep learning
• 10.1. Basic concepts
• 10.2. Improve training of deep learning models
• 10.3. Convolutional neural networks
• 10.4. Recurrent neural networks
• 10.5. Graph neural networks
• 10.6. Summary
• 10.7. Exercises
• 10.8. Bibliographic notes
• Bibliography
• Chapter 11: Outlier detection
• 11.1. Basic concepts
• 11.2. Statistical approaches
• 11.3. Proximity-based approaches
• 11.4. Reconstruction-based approaches
• 11.5. Clustering- vs. classification-based approaches
• 11.6. Mining contextual and collective outliers
• 11.7. Outlier detection in high-dimensional data
• 11.8. Summary
• 11.9. Exercises
• 11.10. Bibliographic notes
• Bibliography
• Chapter 12: Data mining trends and research frontiers
• 12.1. Mining rich data types
• 12.2. Data mining applications
• 12.3. Data mining methodologies and systems
• 12.4. Data mining, people, and society
• Bibliography
• Appendix A: Mathematical background
• 1.1. Probability and statistics
• 1.2. Numerical optimization
• 1.3. Matrix and linear algebra
• 1.4. Concepts and tools from signal processing
• 1.5. Bibliographic notes
• Bibliography
• Bibliography
• Bibliography
• Index