Microarray Time-Series Data Clustering Using Rough-Fuzzy C-Means Algorithm

Abstract

Clustering is one of the important analysis in functional genomics that discovers groups of co-expressed genes from microarray data. In this paper, the application of rough-fuzzy c-means (RFCM) algorithm is presented to discover co-expressed gene clusters. One of the major issues of the RFCM based microarray data clustering is how to select initial prototypes of different clusters. To overcome this limitation, a method is proposed to select initial cluster centers. It enables the RFCM algorithm to converge to an optimum or near optimum solutions and helps to discover co-expressed gene clusters. A method is also introduced based on Dunn's cluster validity index to identify optimum values of different parameters of the initialization method and the RFCM algorithm. The effectiveness of the RFCM algorithm, along with a comparison with other related methods, is demonstrated on five yeast gene expression time-series data sets using Silhouette index, Davies-Bouldin index, and gene ontology based analysis.