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Clustering algorithms in bioinformatics are mostly used to decrypt the salient data in gene expression which is used to acknowledge biological processes in an organism. Gene expression exhibits divergent nature under varying clinical conditions, different tissues and different organisms. The observations drawn from the above conditions enhance the study and analysis of gene functionality. This in turn supports in drug discovery based on the diseased area and the varying nature of genes with diseased condition [28]. With the presence of voluminous amount of genes, it is complex to interpret the data. Therefore, the hidden patterns are being revealed by applying clustering techniques providing a better understanding of functioning of genes and the cellular and biological process of a cell as well [9]. Clustering can be either hard or overlapping. Contemplating every gene as a single cluster is referred as hard clustering whereas overlapping cluster is defined as degree of integration of relatable genes in diverse clusters to every gene expression [10].