Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues

Abstract

Clustering algorithms have been successfully applied to several data analysis problems in a wide range of domains, such as image processing, bioinformatics, crude oil analysis, market segmentation, document categorization, and web mining. The need for organizing data into categories of similar objects has made the task of clustering very important to these domains. In this context, there has been an increasingly interest in the study of evolutionary algorithms for clustering, especially those algorithms capable of finding blurred clusters that are not clearly separated from each other. In particular, a number of evolutionary algorithms for fuzzy clustering have been addressed in the literature. This chapter has two main contributions. First, it presents an overview of evolutionary algorithms designed for fuzzy clustering. Second, it describes a fuzzy version of an evolutionary algorithm for clustering, which has shown to be more computationally efficient than systematic (i.e., repetitive) approaches when the number of clusters in a data set is unknown. Illustrative experiments showing the influence of local optimization on the efficiency of the evolutionary search are also presented. These experiments reveal interesting aspects of the effect of an important parameter found in many evolutionary algorithms for clustering, namely, the number of iterations of a given local search procedure to be performed at each generation.