Abstract
Clustering of gene expression profiles is a mandatory task in cancer classification. Querying the expression of thousands of genes simultaneously imposes the use of powerful clustering techniques. Swarm based methods have shown their ability to perform data clustering. However, they may be faced to premature convergence problem and may be time consuming when large data sets need to be processed. Nowadays, the availability and widespread of parallel processing resources make possible the use of cooperative parallel methods. Within this context, we propose in this paper an archipelago based model that allows to reap advantage from the dynamics and the intrinsic parallelism of three swarm based methods namely PSO, ABC and ACO. Cooperation is achieved by sharing information through migration inside and between archipelagoes. The proposed cooperative parallel model for clustering gene expression profiles has been implemented on multicore computers and applied to several data sets. Experimental results show that it competes and even outperforms existing methods.