Metaheuristic Clustering

  • Swagatam Das
  • Ajith Abraham
  • Amit Konar

Part of the Studies in Computational Intelligence book series (SCI, volume 178)

Table of contents

  1. Front Matter
  2. Swagatam Das, Ajith Abraham, Amit Konar
    Pages 1-62
  3. Swagatam Das, Ajith Abraham, Amit Konar
    Pages 63-110
  4. Swagatam Das, Ajith Abraham, Amit Konar
    Pages 137-174
  5. Swagatam Das, Ajith Abraham, Amit Konar
    Pages 213-238
  6. Swagatam Das, Ajith Abraham, Amit Konar
    Pages 239-247
  7. Back Matter

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.


algorithms data mining evolution heuristics kernel knowledge learning metaheuristic modeling neural network optimization

Authors and affiliations

  • Swagatam Das
    • 1
  • Ajith Abraham
    • 2
  • Amit Konar
    • 1
  1. 1.Jadavpur UniversityCalcuttaIndia
  2. 2.Norwegian University of Science and TechnologyTrondheimNorway

Bibliographic information

  • DOI
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-92172-1
  • Online ISBN 978-3-540-93964-1
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site