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A survey of evolutionary algorithms for clustering

Hruschka, Eduardo Raul, Campello, Ricardo J. G. B., Freitas, Alex A., de Carvalho, Andre C.P.L.F. (2009) A survey of evolutionary algorithms for clustering. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 39 (2). pp. 133-155. ISSN 1094-6977. (doi:10.1109/TSMCC.2008.2007252) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:24074)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
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This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context, most of the paper is devoted to partitional algorithms that look for hard clusterings of data, though overlapping (i.e., soft and fuzzy) approaches are also covered in the paper. The paper is original in what concerns two main aspects. Firsts it provides an up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics like multiobjective and ensemble-based evolutionary clustering. Second, it provides a taxonomy that highlights some very important aspects in the context of evolutionary data clustering, namely, fixed or variable number of clusters, cluster-oriented or nonoriented operators, context-sensitive or context-insensitive operators, guided or unguided operators, binary, integer, or real encodings, centroid-based, medoid-based, label-based, tree-based, or graph-based representations, among others. A number of references are provided that describe applications of evolutionary algorithms for clustering in different domains, such as image processing, computer security, and bioinformatics. The paper ends by addressing some important issues and open questions that can be subject of future research.

Item Type: Article
DOI/Identification number: 10.1109/TSMCC.2008.2007252
Uncontrolled keywords: data mining, clustering, evolutionary algorithms
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 29 Mar 2010 12:13 UTC
Last Modified: 16 Nov 2021 10:02 UTC
Resource URI: (The current URI for this page, for reference purposes)

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