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Foundations of Computational, IntelligenceVolume 6

Data Mining

  • Ajith Abraham
  • Aboul-Ella Hassanien
  • André Ponce de Leon F. de Carvalho
  • Václav Snášel

Part of the Studies in Computational Intelligence book series (SCI, volume 206)

Table of contents

  1. Front Matter
  2. Data Click Streams and Temporal Data Mining

    1. Front Matter
      Pages 1-1
    2. H. Hannah Inbarani, K. Thangavel
      Pages 3-27
    3. João Gama, Pedro Pereira Rodrigues
      Pages 29-45
    4. Mohamed Medhat Gaber
      Pages 47-66
    5. Paul Cotofrei, Kilian Stoffel
      Pages 67-96
    6. Yasufumi Takama, Yuki Muto
      Pages 97-123
  3. Text and Rule Mining

    1. Front Matter
      Pages 125-125
    2. Josef Steinberger, Karel Ježek
      Pages 127-149
    3. Yee Mey Goh, Matt Giess, Chris McMahon, Ying Liu
      Pages 151-169
    4. Thomas W. H. Lui, David K. Y. Chiu
      Pages 171-191
    5. Monica Chiş, Soumya Banerjee, Aboul Ella Hassanien
      Pages 193-207
    6. Bahman Yari Saeed Khanloo, Daryanaz Dargahi, Nima Aghaeepour, Ali Masoudi-Nejad
      Pages 209-227
  4. Data Mining Applications

    1. Front Matter
      Pages 263-263
    2. Martin Řimnáč, Roman Špánek
      Pages 265-296
    3. A. Awasthi, S. S. Chauhan, M. Parent, Y. Lechevallier, J. M. Proth
      Pages 297-320
    4. Alexei Manso Corrêa Machado
      Pages 321-344
    5. Marco Lackovic, Domenico Talia, Paolo Trunfio
      Pages 345-369
    6. N. Karthikeyani Visalakshi, K. Thangavel
      Pages 371-397
  5. Back Matter

About this book

Introduction

Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, business, health care, banking, retail, and many others. Advanced representation schemes and computational intelligence techniques such as rough sets, neural networks; decision trees; fuzzy logic; evolutionary algorithms; artificial immune systems; swarm intelligence; reinforcement learning, association rule mining, Web intelligence paradigms etc. have proved valuable when they are applied to Data Mining problems. Computational tools or solutions based on intelligent systems are being used with great success in Data Mining applications. It is also observed that strong scientific advances have been made when issues from different research areas are integrated.

This Volume comprises of 15 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Intelligence techniques for Data Mining.

Keywords

algorithm algorithms bioinformatics classification computational intelligence data mining decision tree evolution intelligence knowledge discovery machine learning model neural networks semantic web statistics

Editors and affiliations

  • Ajith Abraham
    • 1
  • Aboul-Ella Hassanien
    • 2
  • André Ponce de Leon F. de Carvalho
    • 3
  • Václav Snášel
    • 4
  1. 1.Machine Intelligence Research Labs, (MIR Labs), Scientific Network for Innovation and Research ExcellenceWashingtonUSA
  2. 2.College of Business Administration, Quantitative and Information System DepartmentKuwait UniversitySafatKuwait
  3. 3.Department of Computer ScienceUniversity of São PauloSao CarlosBrazil
  4. 4.Dept. Computer ScienceTechnical University OstravaOstravaCzech Republic

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-01091-0
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-01090-3
  • Online ISBN 978-3-642-01091-0
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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