6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
by Micheline Kamber, Jian Pei, Jiawei Han
Data Mining: Concepts and Techniques, 3rd Edition
- Cover image
- Title page
- Table of Contents
- Front Matter
- Copyright
- Dedication
- Foreword
- Foreword to Second Edition
- Preface
- Acknowledgments
- About the Authors
- 1. Introduction
- Publisher Summary
- 1.1 Why Data Mining?
- 1.2 What Is Data Mining?
- 1.3 What Kinds of Data Can Be Mined?
- 1.4 What Kinds of Patterns Can Be Mined?
- 1.5 Which Technologies Are Used?
- 1.6 Which Kinds of Applications Are Targeted?
- 1.7 Major Issues in Data Mining
- 1.8 Summary
- 1.9 Exercises
- 1.10 Bibliographic Notes
- 2. Getting to Know Your Data
- 3. Data Preprocessing
- 4. Data Warehousing and Online Analytical Processing
- 5. Data Cube Technology
- 6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
- 7. Advanced Pattern Mining
- Publisher Summary
- 7.1 Pattern Mining: A Road Map
- 7.2 Pattern Mining in Multilevel, Multidimensional Space
- 7.3 Constraint-Based Frequent Pattern Mining
- 7.4 Mining High-Dimensional Data and Colossal Patterns
- 7.5 Mining Compressed or Approximate Patterns
- 7.6 Pattern Exploration and Application
- 7.7 Summary
- 7.8 Exercises
- 7.9 Bibliographic Notes
- 8. Classification: Basic Concepts
- 9. Classification: Advanced Methods
- Publisher Summary
- 9.1 Bayesian Belief Networks
- 9.2 Classification by Backpropagation
- 9.3 Support Vector Machines
- 9.4 Classification Using Frequent Patterns
- 9.5 Lazy Learners (or Learning from Your Neighbors)
- 9.6 Other Classification Methods
- 9.7 Additional Topics Regarding Classification
- Summary
- 9.9 Exercises
- 9.10 Bibliographic Notes
- 10. Cluster Analysis: Basic Concepts and Methods
- 11. Advanced Cluster Analysis
-
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