1. Department of Computer Sciences, University of S?o Paulo at S?o Carlos, C.P. 668, CEP 13560-970, S?o-Carlos, SP, Brazil 2. COPOP/UniSantos, R. Carvalho de Mendon?a 144, CEP 11070-906, Santos, SP, Brazil
Abstract:
This 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.