Data Mining Concepts and Techniques Jiawin Han, Jian Pei & Hanghang Tong English

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