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Traditional clustering methods are limited when dealing with huge andheterogeneous groups of gene expression data, which motivates the developmentof bi-clustering methods. Bi-clustering methods are used to mine bi-clusterswhose subsets of samples (genes) are co-regulated under their test conditions.Studies show that mining bi-clusters of consistent trends and trends withsimilar degrees of fluctuations from the gene expression data is essential inbioinformatics research. Unfortunately, traditional bi-clustering methods arenot fully effective in discovering such bi-clusters. Therefore, we propose anovel bi-clustering method by involving here the theory of Granular Computing.In the proposed scheme, the gene data matrix, considered as a group of timeseries, is transformed into a series of ordered information granules. With theinformation granules we build a characteristic matrix of the gene data tocapture the fluctuation trend of the expression value between consecutiveconditions to mine the ideal bi-clusters. The experimental results are inagreement with the theoretical analysis, and show the excellent performance ofthe proposed method.

A Novel Granular-Based Bi-Clustering Method of Deep Mining the Co-Expressed Genes