© 2005

Data Mining and Knowledge Discovery Handbook

  • Oded Maimon
  • Lior Rokach
  • Most complete, extensive and modern handbook available today in the field of data mining, the core of the knowledge discovery process

  • Algorithmic descriptions are detailed so the reader can understand exactly how they work, and thus implement, modify and intelligently use them

  • Includes detailed tutorials, and each topic is supplemented with references for further study


Table of contents

  1. Front Matter
    Pages i-xxxv
  2. Introduction to Knowledge Discovery in Databases

    1. Oded Maimon, Lior Rokach
      Pages 1-17
  3. Preprocessing Methods

    1. Jonathan I. Maletic, Andrian Marcus
      Pages 21-36
    2. Jerzy W. Grzymala-Busse, Witold J. Grzymala-Busse
      Pages 37-57
    3. Barak Chizi, Oded Maimon
      Pages 93-111
    4. Ying Yang, Geoffrey I. Webb, Xindong Wu
      Pages 113-130
    5. Irad Ben-Gal
      Pages 131-146
  4. Supervised Methods

    1. Oded Maimon, Lior Rokach
      Pages 149-164
    2. Lior Rokach, Oded Maimon
      Pages 165-192
    3. Paola Sebastiani, Maria M. Abad, Marco F. Ramoni
      Pages 193-230
    4. Richard A. Berk
      Pages 231-255
    5. Armin Shmilovici
      Pages 257-276
    6. Jerzy W. Grzymala-Busse
      Pages 277-294
  5. Unsupervised Methods

    1. Lior Rokach, Oded Maimon
      Pages 321-352
    2. Frank Höppner
      Pages 353-376
    3. Bart Goethals
      Pages 377-397
    4. Jean-Francois Boulicaut, Baptiste Jeudy
      Pages 399-416
    5. Steve Donoho
      Pages 417-432

About this book


Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository.

This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security.

Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.


Bayesian networks KAP_D018 KDD KLT KLTcatalog algorithm currentjm data mining data mining applications decision trees ensemble method knowledge discovery large datasets preprocessing method soft computing method statistical method text min

Editors and affiliations

  • Oded Maimon
    • 1
  • Lior Rokach
    • 1
  1. 1.Dept. of Industrial EngineeringTel-Aviv UniversityRamat-AvivIsrael

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