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Studies in Computational Intelligence

Metaheuristic Clustering

Authors: Das, Swagatam, Abraham, Ajith, Konar, Amit

  • Latest research on metaheuristic clustering

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About this book

Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention.

In this Volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolution algorithm when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. This volume comprises of 7 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges.

Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable.

Reviews

From the reviews:

β€œIn this volume, the performance of DE is illustrated, when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. … The reader is carefully navigated through the efficacies of clustering, evolutionary optimization and a hybridization of the both.” (T. Postelnicu, Zentralblatt MATH, Vol. 1221, 2011)

Table of contents (7 chapters)

  • Metaheuristic Pattern Clustering – An Overview

    Das, Swagatam (et al.)

    Pages 1-62

  • Differential Evolution Algorithm: Foundations and Perspectives

    Das, Swagatam (et al.)

    Pages 63-110

  • Modeling and Analysis of the Population-Dynamics of Differential Evolution Algorithm

    Das, Swagatam (et al.)

    Pages 111-135

  • Automatic Hard Clustering Using Improved Differential Evolution Algorithm

    Das, Swagatam (et al.)

    Pages 137-174

  • Fuzzy Clustering in the Kernel-Induced Feature Space Using Differential Evolution Algorithm

    Das, Swagatam (et al.)

    Pages 175-211

Buy this book

eBook $159.00
price for USA (gross)
  • ISBN 978-3-540-93964-1
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $209.00
price for USA
  • ISBN 978-3-540-92172-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $209.00
price for USA
  • ISBN 978-3-642-10071-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Metaheuristic Clustering
Authors
Series Title
Studies in Computational Intelligence
Series Volume
178
Copyright
2009
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-540-93964-1
DOI
10.1007/978-3-540-93964-1
Hardcover ISBN
978-3-540-92172-1
Softcover ISBN
978-3-642-10071-0
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XVIII, 252
Topics