Data Mining

A Knowledge Discovery Approach

By Krzysztof J. Cios , Witold Pedrycz , Roman W. Swiniarski , Lukasz Andrzej Kurgan

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This comprehensive textbook on data mining details the unique steps of the knowledge discovery process - an industry standard that prescribes the sequence in which projects should be performed, from data understanding and preprocessing to deployment of the results.

Full Description

  • ISBN13: 978-0-3873-3333-5
  • 621 Pages
  • User Level: Students
  • Publication Date: October 5, 2007
  • Available eBook Formats: PDF
  • eBook Price: $99.00
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Full Description
This comprehensive, senior through graduate level textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed. The book 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. The text 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. Researchers, practitioners and students are certain to consider Data Mining an indispensable resource in successfully accomplishing the goals of their data mining projects.
Table of Contents

Table of Contents

  1. Part I. Data Mining and Knowledge Discovery: Introduction.
  2. Knowledge Discovery Process.
  3. Part II. Data Understanding: Data.
  4. Concepts of Learning, Classification and Regression.
  5. Knowledge Representation.
  6. Part III. Data Preprocessing: Databases, Data Warehouses and OLAP.
  7. Feature Extraction and Selection Methods.
  8. Discretization Methods.
  9. Part IV. Data Mining: Methods for Constructing Data Models: Unsupervised Learning: Clustering.
  10. Unsupervised Learning: Association Rules.
  11. Supervised Learning: Statistical Methods.
  12. Supervised Learning: Decision Trees, Rule Algorithms and Their Hybrids.
  13. Supervised Learning: Neural Networks.
  14. Text Mining.
  15. Part V. Data Models Assessment: Assessment of Data Models.
  16. Part VI Data Security and Privacy Issues: Security, Privacy and Data Mining.
  17. Appendices: Overview of key mathematical concepts.
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