Data mining : a knowledge discovery approach / Krzysztof J. Cios... [et al.].

Includes bibliographical references and index.

Chapter 1. Introduction -- Chapter 2. The Knowledge Discovery Process -- Chapter 3. Data -- Chapter 4. Concepts of Learning, Classification, and Regression -- Chapter 5. Knowledge Representation -- Chapter 6. Databases, Data Warehouses, and OLAP -- Chapter 7. Feature Extraction and Selection Methods -- Chapter 8. Discretization Methods -- Chapter 9. Unsupervised Learning: Clustering -- Chapter 10. Unsupervised Learning: Association Rules -- Chapter 11. Supervised Learning: Statistical Methods -- Chapter 12. Supervised Learning: Decision Trees, Rule Algorithms, and Their Hybrids -- Chapter 13. Supervised Learning: Neural Networks -- Chapter 14. Text Mining -- Chapter 15. Assessment of Data Models -- Chapter 16. Data Security, Privacy and Data Mining.

This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribe the sequence in which data mining projects should be performed. Data Mining offers an authoritative treatment of all development phases from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes this book from other texts in the area. It concentrates on data preparation, clustering and association rule learning (required for processing unsupervised data), decision trees, rule induction algorithms, neural networks, and many other data mining methods, focusing predominantly on those which have proven successful in data mining projects.

1