Principles of Data Mining


The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.

The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

Table of Contents

  1. Full Contents
  2. List of Tables
  3. List of Figures
  4. Series Foreword
  5. Preface
  6. 1. Introduction
  7. 2. Measurement and Data
  8. 3. Visualizing and Exploring Data
  9. 4. Data Analysis and Uncertainty
  10. 5. A Systematic Overview of Data Mining Algorithms
  11. 6. Models and Patterns
  12. 7. Score Functions for Data Mining Algorithms
  13. 8. Search and Optimization Methods
  14. 9. Descriptive Modeling
  15. 10. Predictive Modeling for Classification
  16. 11. Predictive Modeling for Regression
  17. 12. Data Organization and Databases
  18. 13. Finding Patterns and Rule
  19. 14. Retrieval by Content
  20. Appendix: Random Variables
  21. References
  22. Index