A rough set based novel biclustering algorithm for gene expression data

Microarray technology has emerged as a boon to simultaneously monitor the expression levels of thousands of genes across collections of related samples. The main goal in the analysis of large and heterogeneous gene expression datasets is to identify groups of genes that get expressed in a set of experimental conditions. Several clustering techniques have been proposed for identifying gene signatures and to understand their role and many of them have been applied to gene expression data, but with partial success. This paper proposes to develop a novel biclustering technique (RBGED) that is based on rough set theory. This algorithm simultaneously clusters both the rows and columns of a data matrix. The advantage is that it overcomes the restriction of one object belonging to only one cluster. This algorithm is intelligent because it automatically determines the optimum number of clusters. A theoretical understanding of the proposed algorithm is analyzed and case studied with Rough Fuzzy k means algorithm.