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Swarm Intelligence for Multi-objective Problems in Data Mining

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  • © 2009

Overview

Editors:
  1. Carlos Artemio Coello Coello

    1. Departamento de Computacion, CINVESTAV-IPN, Mexico, 07360

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  2. Satchidananda Dehuri

    1. Department of information and Communication Technology, FakirMohan University, Balasore, India

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  3. Susmita Ghosh

    1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

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Part of the book series: Studies in Computational Intelligence (SCI, volume 242)

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Table of contents (11 chapters)

  1. Front Matter

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  2. An Introduction to Swarm Intelligence for Multi-objective Problems

    • Satchidananda Dehuri, Susmita Ghosh, Carlos A. Coello Coello
    Pages 1-17

  3. Multi-Criteria Ant Feature Selection Using Fuzzy Classifiers

    • Susana M. Vieira, João M. C. Sousa, Thomas A. Runkler
    Pages 19-36

  4. Multiobjective Particle Swarm Optimization in Classification-Rule Learning

    • Augusto de Almeida Prado G. Torácio
    Pages 37-64

  5. Using Multi-Objective Particle Swarm Optimization for Designing Novel Classifiers

    • Seyed-Hamid Zahiri, Seyed-Alireza Seyedin
    Pages 65-92

  6. Optimizing Decision Trees Using Multi-objective Particle Swarm Optimization

    • Jonathan E. Fieldsend
    Pages 93-114

  7. A Discrete Particle Swarm for Multi-objective Problems in Polynomial Neural Networks used for Classification: A Data Mining Perspective

    • Satchidananda Dehuri, Carlos A. Coello Coello, Sung-Bae Cho, Ashish Ghosh
    Pages 115-155

  8. Rigorous Runtime Analysis of Swarm Intelligence Algorithms – An Overview

    • Carsten Witt
    Pages 157-177

  9. Mining Rules: A Parallel Multiobjective Particle Swarm Optimization Approach

    • André B. de Carvalho, Aurora Pozo
    Pages 179-198

  10. The Basic Principles of Metric Indexing

    • Magnus Lie Hetland
    Pages 199-232

  11. Particle Evolutionary Swarm Multi-Objective Optimization for Vehicle Routing Problem with Time Windows

    • Angel Muñoz-Zavala, Arturo Hernández-Aguirre, Enrique Villa-Diharce
    Pages 233-257

  12. Combining Correlated Data from Multiple Classifiers

    • Lisa Osadciw, Nisha Srinivas, Kalyan Veeramachaneni
    Pages 259-281

  13. Back Matter

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Keywords

About this book

The purpose of this book is to collect contributions that are at the intersection of multi-objective optimization, swarm intelligence (specifically, particle swarm optimization and ant colony optimization) and data mining. Such a collection intends to illustrate the potential of multi-objective swarm intelligence techniques in data mining, with the aim of motivating more researchers in evolutionary computation and machine learning to do research in this field.

This volume consists of eleven chapters, including an introduction that provides the basic concepts of swarm intelligence techniques and a discussion of their use in data mining. Some of the research challenges that must be faced when using swarm intelligence techniques in data mining are also addressed. The rest of the chapters were contributed by leading researchers, and were organized according to the steps normally followed in Knowledge Discovery in Databases (KDD) (i.e., data preprocessing, data mining, and post processing).

We hope that this book becomes a valuable reference for those wishing to do research on the use of multi-objective swarm intelligence techniques in data mining and knowledge discovery in databases.

Editors and Affiliations

  • Departamento de Computacion, CINVESTAV-IPN, Mexico, 07360

    Carlos Artemio Coello Coello

  • Department of information and Communication Technology, FakirMohan University, Balasore, India

    Satchidananda Dehuri

  • Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Susmita Ghosh

Bibliographic Information

  • Book Title: Swarm Intelligence for Multi-objective Problems in Data Mining

  • Editors: Carlos Artemio Coello Coello, Satchidananda Dehuri, Susmita Ghosh

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-642-03625-5

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2009

  • Hardcover ISBN: 978-3-642-03624-8Published: 28 September 2009

  • Softcover ISBN: 978-3-642-26053-7Published: 14 March 2012

  • eBook ISBN: 978-3-642-03625-5Published: 01 October 2009

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XIV, 287

  • Topics: Mathematical and Computational Engineering, Artificial Intelligence

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