Clustering with evolution strategies

Babu, Phanendra G and Murty, Narasimha M (1994) Clustering with evolution strategies. In: Pattern Recognition, 27 (2). pp. 321-329.

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Abstract

Tbe applicability of evolution strategies (ESs), population based stochastic optimization techniques, to optimize clustering objective functions is explored. Clustering objective functions are categorized into centroid and non-centroid type of functions. Optimization of the centroid type of objective functions is accomplished by formulating them as functions of real-valued parameters using ESs. Both hard and fuzzy clustering objective functions are considered in this study. Applicability of ESs to discrete optimization problems is extended to optimize the non-centroid type of objective functions. As ESs are amenable to parallelization, a parallel model (master/slave model) is described in the context of the clustering problem. Results obtained for selected data sets substantiate the utility of ESs in clustering.

Item Type:	Journal Article
Publication:	Pattern Recognition
Publisher:	Elsevier
Additional Information:	Copyright of this article belongs to Elsevier.
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	16 Oct 2006
Last Modified:	19 Sep 2010 04:30
URI:	http://eprints.iisc.ac.in/id/eprint/8247

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