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

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets.

After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classification and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining.