文档介绍：Table of Contents
Cover Image
Front Matter
Copyright
Dedication
Foreword
Foreword to Second Edition
Preface
Acknowledgments
About the Authors
1. Introduction
. Why Data Mining?
. What Is Data Mining?
. What Kinds of Data Can Be Mined?
. What Kinds of Patterns Can Be Mined?
. Which Technologies Are Used?
. Which Kinds of Applications Are Targeted?
. Major Issues in Data Mining
. Summary
. Exercises
. Bibliographic Notes
2. Getting to Know Your Data
. Data Objects and Attribute Types
. Basic Statistical Descriptions of Data
. Data Visualization
. Measuring Data Similarity and Dissimilarity
. Summary
. Exercises
. Bibliographic Notes
3. Data Preprocessing
. Data Preprocessing: An Overview
. Data Cleaning
. Data Integration
. Data Reduction
. Data Transformation and Data Discretization
. Summary
. Exercises
. Bibliographic Notes
4. Data Warehousing and Online Analytical Processing
. Data Warehouse: Basic Concepts
. Data Warehouse Modeling: Data Cube and OLAP
. Data Warehouse Design and Usage
. Data Warehouse Implementation
. Data Generalization by Attribute-Oriented Induction
. Summary
. Exercises
5. Data Cube Technology
. Data putation: Preliminary Concepts
. Data putation Methods
. Processing Advanced Kinds of Queries by Exploring Cube Technology
. Multidimensional Data Analysis in Cube Space
. Summary
. Exercises
. Bibliographic Notes
6. Mining Frequent Patterns, Associations, and Correlations
. Basic Concepts
. Frequent Itemset Mining Methods
. Which Patterns Are Interesting?—Pattern Evaluation Methods
. Summary
. Exercises
. Bibliographic Notes
7. Advanced Pattern Mining
. Pattern Mining: A Road Map
. Pattern Mining in Multilevel, Multidimensiona