  Traditional clustering methods are limited when dealing with huge and
heterogeneous groups of gene expression data, which motivates the development
of bi-clustering methods. Bi-clustering methods are used to mine bi-clusters
whose subsets of samples (genes) are co-regulated under their test conditions.
Studies show that mining bi-clusters of consistent trends and trends with
similar degrees of fluctuations from the gene expression data is essential in
bioinformatics research. Unfortunately, traditional bi-clustering methods are
not fully effective in discovering such bi-clusters. Therefore, we propose a
novel bi-clustering method by involving here the theory of Granular Computing.
In the proposed scheme, the gene data matrix, considered as a group of time
series, is transformed into a series of ordered information granules. With the
information granules we build a characteristic matrix of the gene data to
capture the fluctuation trend of the expression value between consecutive
conditions to mine the ideal bi-clusters. The experimental results are in
agreement with the theoretical analysis, and show the excellent performance of
the proposed method.
