On the efficiency of evolutionary fuzzy clustering

dc.creatorCAMPELLO, Ricardo J. G. B.
dc.creatorHRUSCHKA, Eduardo R.
dc.creatorALVES, Vinicius S.
dc.date.accessioned2012-10-20T03:30:58Z
dc.date.accessioned2018-07-04T15:38:00Z
dc.date.available2012-10-20T03:30:58Z
dc.date.available2018-07-04T15:38:00Z
dc.date.created2012-10-20T03:30:58Z
dc.date.issued2009
dc.identifierJOURNAL OF HEURISTICS, v.15, n.1, p.43-75, 2009
dc.identifier1381-1231
dc.identifierhttp://producao.usp.br/handle/BDPI/28780
dc.identifier10.1007/s10732-007-9059-6
dc.identifierhttp://dx.doi.org/10.1007/s10732-007-9059-6
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1625422
dc.description.abstractThis paper tackles the problem of showing that evolutionary algorithms for fuzzy clustering can be more efficient than systematic (i.e. repetitive) approaches when the number of clusters in a data set is unknown. To do so, a fuzzy version of an Evolutionary Algorithm for Clustering (EAC) is introduced. A fuzzy cluster validity criterion and a fuzzy local search algorithm are used instead of their hard counterparts employed by EAC. Theoretical complexity analyses for both the systematic and evolutionary algorithms under interest are provided. Examples with computational experiments and statistical analyses are also presented.
dc.languageeng
dc.publisherSPRINGER
dc.relationJournal of Heuristics
dc.rightsCopyright SPRINGER
dc.rightsrestrictedAccess
dc.subjectFuzzy clustering
dc.subjectEvolutionary algorithms
dc.subjectComplexity analyses
dc.subjectPerformance comparison
dc.titleOn the efficiency of evolutionary fuzzy clustering
dc.typeArtículos de revistas


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