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Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics.
Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.
Table of Content
1. Introduction
2. Data
3. Exploring Data
4. Classification: Basic Concepts, Decision Trees, and Model Evaluation
5. Classification: Alternative Techniques
6. Association Analysis: Basic Concepts and Algorithms
7. Association Analysis: Advanced Concepts
8. Cluster Analysis: Basic Concepts and Algorithms
9. Cluster Analysis: Additional Issues and Algorithms
10. Anomaly Detection
Appendix B: Dimensionality Reduction
Appendix D: Regression
Appendix E: Optimization
Salient Features
• Provides both theoretical and practical coverage of all data mining topics.
• Includes extensive number of integrated examples and figures.
• Offers instructor resources including solutions for exercises and complete set of lecture slides.
• Assumes only a modest statistics or mathematics background, and no database knowledge is needed.
• Topics covered include; predictive modeling, association analysis, clustering, anomaly detection, visualization. ISBN - 9789332518650
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Pages : 736
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