Product details:

• ISBN-10 ‏ : ‎ 9780123814791
• ISBN-13 ‏ : ‎ 978-9380931913
• Author: Jiawei Han, Micheline Kamber, Jian Pei 

Book annotation not available for this Data Han, Jiawei/ Kamber, Micheline/ Pei, Elsevier Science LtdPublication 2011/06/22Number of 703Binding HARDCOVERLibrary of 2011010635

Table of contents:

• Dedication
• Foreword
• Foreword to Second Edition
• Preface
• Organization of the Book
• To the Instructor
• To the Student
• To the Professional
• Book Web Sites with Resources
• Acknowledgments
• Third Edition of the Book
• Second Edition of the Book
• First Edition of the Book
• 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
  • Publisher Summary
  • 2.1 Data Objects and Attribute Types
  • 2.2 Basic Statistical Descriptions of Data
  • 2.3 Data Visualization
  • 2.4 Measuring Data Similarity and Dissimilarity
  • 2.5 Summary
  • 2.6 Exercises
  • 2.7 Bibliographic Notes
• 3. Data Preprocessing
  • Publisher Summary
  • 3.1 Data Preprocessing: An Overview
  • 3.2 Data Cleaning
  • 3.3 Data Integration
  • 3.4 Data Reduction
  • 3.5 Data Transformation and Data Discretization
  • 3.6 Summary
  • 3.7 Exercises
  • 3.8 Bibliographic Notes
• 4. Data Warehousing and Online Analytical Processing
  • Publisher Summary
  • 4.1 Data Warehouse: Basic Concepts
  • 4.2 Data Warehouse Modeling: Data Cube and OLAP
  • 4.3 Data Warehouse Design and Usage
  • 4.4 Data Warehouse Implementation
  • 4.5 Data Generalization by Attribute-Oriented Induction
  • 4.6 Summary
  • 4.7 Exercises
  • Bibliographic Notes
• 5. Data Cube Technology
  • Publisher Summary
  • 5.1 Data Cube Computation: Preliminary Concepts
  • 5.2 Data Cube Computation Methods
  • 5.3 Processing Advanced Kinds of Queries by Exploring Cube Technology
  • 5.4 Multidimensional Data Analysis in Cube Space
  • 5.5 Summary
  • 5.6 Exercises
  • 5.7 Bibliographic Notes
• 6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
  • Publisher Summary
  • 6.1 Basic Concepts
  • 6.2 Frequent Itemset Mining Methods
  • 6.3 Which Patterns Are Interesting?—Pattern Evaluation Methods
  • 6.4 Summary
  • 6.5 Exercises
  • 6.6 Bibliographic Notes
• 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
  • Publisher Summary
  • 8.1 Basic Concepts
  • 8.2 Decision Tree Induction
  • 8.3 Bayes Classification Methods
  • 8.4 Rule-Based Classification
  • 8.5 Model Evaluation and Selection
  • 8.6 Techniques to Improve Classification Accuracy
  • 8.7 Summary
  • 8.8 Exercises
  • 8.9 Bibliographic Notes
• 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
  • Publisher Summary
  • 10.1 Cluster Analysis
  • 10.2 Partitioning Methods
  • 10.3 Hierarchical Methods
  • 10.4 Density-Based Methods
  • 10.5 Grid-Based Methods
  • 10.6 Evaluation of Clustering
  • 10.7 Summary
  • 10.8 Exercises
  • 10.9 Bibliographic Notes
• 11. Advanced Cluster Analysis
  • Publisher Summary
  • 11.1 Probabilistic Model-Based Clustering
  • 11.2 Clustering High-Dimensional Data
  • 11.3 Clustering Graph and Network Data
  • 11.4 Clustering with Constraints
  • Summary
  • 11.6 Exercises
  • 11.7 Bibliographic Notes
• 12. Outlier Detection
  • Publisher Summary
  • 12.1 Outliers and Outlier Analysis
  • 12.2 Outlier Detection Methods
  • 12.3 Statistical Approaches
  • 12.4 Proximity-Based Approaches
  • 12.5 Clustering-Based Approaches
  • 12.6 Classification-Based Approaches
  • 12.7 Mining Contextual and Collective Outliers
  • 12.8 Outlier Detection in High-Dimensional Data
  • 12.9 Summary
  • 12.10 Exercises
  • 12.11 Bibliographic Notes
• 13. Data Mining Trends and Research Frontiers
  • Publisher Summary
  • 13.1 Mining Complex Data Types
  • 13.2 Other Methodologies of Data Mining
  • 13.3 Data Mining Applications
  • 13.4 Data Mining and Society
  • 13.5 Data Mining Trends
  • 13.6 Summary
  • 13.7 Exercises
  • 13.8 Bibliographic Notes
• Bibliography
• Index

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