Supervised descriptive pattern mining

Description

Content: Chapter 1. Introduction to Pattern Mining 1.1 Importance of patterns 1.2 Type of patterns 1.3 Quality measures in pattern mining 1.3.1 Objective interestingness measures 1.3.2 Subjective interestingness measures 1.4 Scalability issues 1.4 Supervised descriptive local patterns Chapter 2. Subgroup Discovery 2.1 Introduction 2.2 Task definition 2.3 Quality measures 2.4 Models in subgroup discovery Chapter 3. Contrast sets 3.1 Introduction 3.2 Task definition 3.3 Algorithms Chapter 4. Emerging patterns 4.1 Introduction 4.2 Task definition 4.3 Algorithms Chapter 5. Class Association rules 5.1 Introduction 5.2 Task definition 5.2.1 Association rules 5.2.2 Class association rules 5.2.3 Associative classification 5.3 Algorithms Chapter 6. Exceptional models 6.1 Introduction 6.2 Exceptional model mining 6.3 Exceptional preference mining 6.4 Exceptional pattern mining 6.5 Algorithms Chapter 7. Applications of supervised descriptive local patterns 7.1 Introduction 7.2 Subgroup discovery 7.3 Contrast sets 7.4 Emerging patterns 7.5 Exceptional models 7.6 Class association rules Chapter 8. Additional tasks related to supervised pattern mining 8.1 Change mining 8.2 Mining of closed sets for labeled data 8.3 Bump hunting 8.4 Impact rules 8.5 Discrimination discovery 8.6 Context aware