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Analysis of microarray data using multiobjective variable string length genetic fuzzy clustering

In this article, a novel multiobjective variable string length real coded genetic fuzzy clustering scheme for clustering microarray gene expression data has been proposed. The proposed technique automatically evolves the number of clusters along with the clustering result. The multiobjective variable string length clustering technique encodes the cluster centers in its chromosomes and simultaneously optimizes two fuzzy validity indices namely PBM index and Xie-Beni validity measure. In the final generation, it produces a set of non-dominated solutions, from which the best solution is selected using Silhouette index which is independent of the number of clusters. The corresponding chromosome length provides the number of clusters. The proposed method is applied on three publicly available real life gene expression data. Superiority of the proposed method over some other well known clustering algorithms has been demonstrated quantitatively.