A cooperative coevolutionary approach to partitional clustering

Abstract

A challenge in partitional clustering is determining the number of clusters that best characterize a set of observations. In this paper, we present a novel approach for determining both an optimal number of clusters and partitioning of the data set. Our new algorithm is based on cooperative coevolution and inspired by the natural process of sympatric speciation. We have evaluated our algorithm on a number of synthetic and real data sets from pattern recognition literature and on a recently-collected set of epigenetic data consisting of DNA methylation levels. In a comparison with a state-of-the-art algorithm that uses a variable string-length GA for clustering, our algorithm demonstrated a significant performance advantage, both in terms of determining an appropriate number of clusters and in the quality of the cluster assignments as reflected by the misclassification rate. © 2010 Springer-Verlag.