Abstract
MicroRNAs form a family of single strand RNA molecules having length of approximately 22 nucleotides that are present in all animals and plants. Various studies have revealed that microRNA tend to cluster on chromosomes. In this regard, a novel clustering algorithm is presented in this paper, integrating rough hypercuboid approach with fuzzy c-means. Using the concept of rough hypercuboid equivalence partition matrix, the lower approximation and boundary region are implicitly computed for the clusters without the need of any user specified threshold. The effectiveness of proposed technique, along with a comparison with existing clustering methods, is presented using some microRNA data sets with the help of several well known cluster validity indices.
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