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

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.