Data Mining and Knowledge Discovery Handbook

  • Oded Maimon
  • Lior Rokach

Table of contents

  1. Front Matter
    Pages i-xx
  2. Oded Maimon, Lior Rokach
    Pages 1-15
  3. Preprocessing Methods

    1. Front Matter
      Pages 17-17
    2. Jonathan I. Maletic, Andrian Marcus
      Pages 19-32
    3. Jerzy W. Grzymala-Busse, Witold J. Grzymala-Busse
      Pages 33-51
    4. Barak Chizi, Oded Maimon
      Pages 83-100
    5. Ying Yang, Geoffrey I. Webb, Xindong Wu
      Pages 101-116
    6. Irad Ben-Gal
      Pages 117-130
  4. Supervised Methods

    1. Front Matter
      Pages 131-131
    2. Lior Rokach, Oded Maimon
      Pages 133-147
    3. Lior Rokach, Oded Maimon
      Pages 149-174
    4. Paola Sebastiani, Maria M. Abad, Marco F. Ramoni
      Pages 175-208
    5. Richard A. Berk
      Pages 209-230
    6. Armin Shmilovici
      Pages 231-247
    7. Jerzy W. Grzymala-Busse
      Pages 249-265
  5. Unsupervised Methods

    1. Front Matter
      Pages 267-267
    2. Lior Rokach
      Pages 269-298
    3. Frank Höppner
      Pages 299-319
    4. Bart Goethals
      Pages 321-338

About this book

Introduction

Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data.

Data Mining and Knowledge Discovery Handbook, Second Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This handbook 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, Second Edition is designed for research scientists, libraries and advanced-level students in computer science and engineering as a reference. This handbook is also suitable for professionals in industry, for computing applications, information systems management, and strategic research management.

Keywords

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
    • 2
  1. 1., Dept. Industrial EngineeringTel Aviv UniversityRamat AvivIsrael
  2. 2., Dept. Information Systems EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-09823-4
  • Copyright Information Springer Science+Business Media, LLC 2010
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-0-387-09822-7
  • Online ISBN 978-0-387-09823-4
  • About this book