DataMining–ConceptsandTechniques - (EPUB全文下载)

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书籍内容：

Table of Contents
Cover Image
Front Matter
Copyright
Dedication
Foreword
Foreword to Second Edition
Preface
Acknowledgments
About the Authors
1. Introduction
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
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
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
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
5. Data Cube Technology
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
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
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
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
9.1. Bayesian Belief Networks
9.2. Classification by Backpropagation
9.3. Support Vector Machines
9.4. Classification Using Fr ............

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