Data Mining and Knowledge Discovery Handbook

  • Oded Maimon
  • Lior Rokach

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
  6. Soft Computing Methods

    1. Alex A. Freitas
      Pages 435-467
    2. Peter G. Zhang
      Pages 487-516
    3. Joseph Komem, Moti Schneider
      Pages 517-533
    4. Tsau Young (’T. Y.’) Lin, Churn-Jung Liau
      Pages 535-561
  7. Supporting Methods

    1. Yoav Benjamini, Moshe Leshno
      Pages 565-587
    2. Petr Hájek
      Pages 589-602
    3. Tao Li, Sheng Ma, Mitsunori Ogihara
      Pages 603-626
    4. Daniel Barbara, Ping Chen
      Pages 627-647
    5. Sigal Sahar
      Pages 649-660
    6. Maria Halkidi, Michalis Vazirgiannis
      Pages 661-696
    7. Paolo Giudici
      Pages 697-714
    8. Jean-Francois Boulicaut, Cyrille Masson
      Pages 715-726
  8. Advanced Methods

    1. Ricardo Vilalta, Christophe Giraud-Carrier, Pavel Brazdil
      Pages 731-748
    2. Gary M. Weiss
      Pages 765-776
    3. Haixun Wang, Philip S. Yu, Jiawei Han
      Pages 777-792
    4. Wei Wang, Jiong Yang
      Pages 793-799
    5. Moty Ben-Dov, Ronen Feldman
      Pages 801-831
    6. Shashi Shekhar, Pusheng Zhang, Yan Huang
      Pages 833-851
    7. Nitesh V. Chawla
      Pages 853-867
    8. Sašo Džeroski
      Pages 869-898
    9. Johannes Fürnkranz
      Pages 899-920
    10. Nora Oikonomakou, Michalis Vazirgiannis
      Pages 921-943
    11. Hong Yao, Cory J. Butz, Howard J. Hamilton
      Pages 945-955
    12. Lior Rokach
      Pages 957-980
    13. Vicenç Torra
      Pages 1005-1016
    14. Antonio Congiusta, Domenico Talia, Paolo Trunfio
      Pages 1017-1041
    15. Steve Moyle
      Pages 1043-1056

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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