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Foundations of Computational Intelligence Volume 4 pp 167–195Cite as

Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 204))

Abstract

Clustering algorithms have been successfully applied to several data analysis problems in a wide range of domains, such as image processing, bioinformatics, crude oil analysis, market segmentation, document categorization, and web mining. The need for organizing data into categories of similar objects has made the task of clustering very important to these domains. In this context, there has been an increasingly interest in the study of evolutionary algorithms for clustering, especially those algorithms capable of finding blurred clusters that are not clearly separated from each other. In particular, a number of evolutionary algorithms for fuzzy clustering have been addressed in the literature. This chapter has two main contributions. First, it presents an overview of evolutionary algorithms designed for fuzzy clustering. Second, it describes a fuzzy version of an evolutionary algorithm for clustering, which has shown to be more computationally efficient than systematic (i.e., repetitive) approaches when the number of clusters in a data set is unknown. Illustrative experiments showing the influence of local optimization on the efficiency of the evolutionary search are also presented. These experiments reveal interesting aspects of the effect of an important parameter found in many evolutionary algorithms for clustering, namely, the number of iterations of a given local search procedure to be performed at each generation.

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Author information

Authors and Affiliations

  1. Department of Computer Sciences of the University of São Paulo at São Carlos (ICMC/USP), São Carlos, SP, Brazil

    D. Horta, M. Naldi, R. J. G. B. Campello, E. R. Hruschka & A. C. P. L. F. de Carvalho

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  1. D. Horta
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  2. M. Naldi
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  3. R. J. G. B. Campello
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  4. E. R. Hruschka
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  5. A. C. P. L. F. de Carvalho
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Editors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn, Washington, USA

    Ajith Abraham

  2. College of Business Administration, Quantitative and Information System Department, Kuwait University, P.O. Box 5486, 13055, Safat, Kuwait

    Aboul-Ella Hassanien

  3. Department of Computer Science, University of São Paulo,SCE - ICMSC - USP, Caixa Postal 668, 13560-970, Sao Carlos, SP, Brazil

    André Ponce de Leon F. de Carvalho

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Horta, D., Naldi, M., Campello, R.J.G.B., Hruschka, E.R., de Carvalho, A.C.P.L.F. (2009). Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues. In: Abraham, A., Hassanien, AE., de Carvalho, A.P.d.L.F. (eds) Foundations of Computational Intelligence Volume 4. Studies in Computational Intelligence, vol 204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01088-0_8

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  • DOI: https://doi.org/10.1007/978-3-642-01088-0_8

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