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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
    5. Jean-Francois Boulicaut, Baptiste Jeudy
      Pages 339-354
    6. Steve Donoho
      Pages 355-368
  6. Soft Computing Methods

    1. Front Matter
      Pages 369-369
    2. Oded Maimon, Shahar Cohen
      Pages 401-417
    3. G. Peter Zhang
      Pages 419-444
    4. Tsau Young (’T. Y.’) Lin, Churn-Jung Liau
      Pages 445-468
    5. Swagatam Das, Ajith Abraham
      Pages 469-504
    6. Lior Rokach
      Pages 505-520
  7. Supporting Methods

    1. Front Matter
      Pages 521-521
    2. Yoav Benjamini, Moshe Leshno
      Pages 523-540
    3. Petr Hájek
      Pages 541-551
    4. Tao Li, Sheng Ma, Mitsunori Ogihara
      Pages 553-571
    5. Noa Ruschin Rimini, Oded Maimon
      Pages 591-601
    6. Maria Halkidi, Michalis Vazirgiannis
      Pages 613-639
    7. Paolo Giudici
      Pages 641-654
    8. Jean-Francois Boulicaut, Cyrille Masson
      Pages 655-664
  8. Advanced Methods

    1. Front Matter
      Pages 665-665
    2. Grigorios Tsoumakas, Ioannis Katakis, Ioannis Vlahavas
      Pages 667-685
    3. Vicenç Torra
      Pages 687-716
    4. Ricardo Vilalta, Christophe Giraud-Carrier, Pavel Brazdil
      Pages 717-731
    5. Gary M. Weiss
      Pages 747-757
    6. Mohamed Medhat Gaber, Arkady Zaslavsky, Shonali Krishnaswamy
      Pages 759-787
    7. Haixun Wang, Philip S. Yu, Jiawei Han
      Pages 789-802
    8. Wei Wang, Jiong Yang
      Pages 803-808
    9. Moty Ben-Dov, Ronen Feldman
      Pages 809-835
    10. Shashi Shekhar, Pusheng Zhang, Yan Huang
      Pages 837-854

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
  • Buy this book on publisher's site