A survey of evolutionary algorithms for clustering

Eduardo Raul Hruschka*, Ricardo J.G.B. Campello, Alex A. Freitas, André C.Ponce Leon F. de Carvalho


Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review


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. First, 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.

TidsskriftIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Udgave nummer2
Sider (fra-til)133-155
StatusUdgivet - 2009
Udgivet eksterntJa

Bibliografisk note

Funding Information:
Manuscript received December 13, 2007; revised April 17, 2008. First published February 13, 2009; current version published February 25, 2009. This work was supported by the Brazilian Research Agencies—National Council for Scientific and Technological Development (CNPq) and São Paulo State Funding Agency (FAPESP). This paper was recommended by Associate Editor J. Lazansky.


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