PSO based feature selection for clustering gene expression data

Gene expression data generated from microarray experiments are characterized by large number of genes or dimensions. Informative gene selection for performing clustering to discover useful phenotypes is a major issue as there is no class information available. In this paper, we propose a wrapper based feature selection approach to perform sample based clustering on gene expression data. The proposed work uses Particle Swarm Optimization(PSO) for best subset generation and k-means as wrapper algorithm for evaluating the subsets. Experimental results show that the features selected by this method is able to produce clusters of good quality. Clustering accuracy of 70–80% were obtained for different datasets.