- Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems.
- Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data.
- Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor.
2. Data Preparation
3. Similarity and Distances
4. Association Pattern Mining
5. Association Pattern Mining: Advanced Concepts
6. Cluster Analysis
7. Cluster Analysis: Advanced Concepts
8. Outlier Analysis
9. Outlier Analysis: Advanced Concepts
10. Data Classification
11. Data Classification: Advanced Concepts
12. Mining Data Streams
13. Mining Text Data
14. Mining Time Series Data
15. Mining Discrete Sequences
16. Mining Spatial Data
17. Mining Graph Data
18. Mining Web Data
19. Social Network Analysis
20. Privacy-Preserving Data Mining
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