A niching genetic k-means algorithm and its applications to gene expression data

Abstract

Partitional clustering is a common approach to cluster analysis. Although many algorithms have been proposed, partitional clustering remains a challenging problem with respect to the reliability and efficiency of recovering high quality solutions in terms of its criterion functions. In this paper, we propose a niching genetic k-means algorithm (NGKA) for partitional clustering, which aims at reliably and efficiently identifying high quality solutions in terms of the sum of squared errors criterion. Within the NGKA, we design a niching method, which encourages mating among similar clustering solutions while allowing for some competitions among dissimilar solutions, and integrate it into a genetic algorithm to prevent premature convergence during the evolutionary clustering search. Further, we incorporate one step of k-means operation into the regeneration steps of the resulted niching genetic algorithm to improve its computational efficiency. The proposed algorithm was applied to cluster both simulated data and gene expression data and compared with previous work. Experimental results clear show that the NGKA is an effective clustering algorithm and outperforms two other genetic algorithm based clustering methods implemented for comparison.