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© 2010

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

About the editors

Prof. Oded Maimon is the Oracle chaired Professor at Tel-Aviv University, Previously at MIT. Oded is a leader expert in the field of data mining and knowledge discovery. He published many articles on new algorithms and seven significant award winning books in the field since 2000. He has also developed and implemented successful applications in the Industry. He heads an international research group sponsored by European Union awards.

Dr. Lior Rokach is a senior lecturer at the Department of Information System Engineering at Ben-Gurion University. He is a recognized expert in intelligent information systems and has held several leading positions in this field. His main areas of interest are Data Mining, Pattern Recognition, and Recommender Systems. Dr. Rokach is the author of over 70 refereed papers in leading journals, conference proceedings and book chapters. In addition he has authored six books and edited three others books.

Bibliographic information

Industry Sectors
Automotive
Chemical Manufacturing
Biotechnology
IT & Software
Telecommunications
Consumer Packaged Goods
Engineering
Pharma
Materials & Steel
Finance, Business & Banking
Electronics
Energy, Utilities & Environment
Aerospace

Reviews

From the reviews of the second edition:

“This handbook provides an excellent guide in every aspect of the discovery process. … Contributors are drawn from noted academic institutions and companies around the world and across diverse disciplines. … serves to define the current state of the art in knowledge discovery, and is particularly useful in cross-fertilization among a diverse set of application scenarios. It is an indispensable reference for researchers and an excellent starting point for advanced students taking graduate courses in this area. Summing Up: Highly recommended. Upper-division undergraduates through professionals/practitioners.” (J. Y. Cheung, Choice, Vol. 48 (10), June, 2011)

“This edition treats new aspects (for instance, privacy) and new methods, like those based on swarm intelligence and multi-label classification. … The book is a comprehensive and detailed reference. … Each chapter contains a long list of references for further investigation. … I recommend this comprehensive book to advanced readers--including designers and architects at software companies--interested in the R&D of data mining.” (K. Balogh, ACM Computing Reviews, November, 2011)