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A great number of biclustering algorithms have been proposed for analyzing gene expression data. Many of them assume to find exclusive biclusters whose subsets of genes are co-regulated under subsets of conditions without intersection. This is not consistent with a general understanding of biological processes that many genes participate in multiple different processes. Therefore nonexclusive biclustering...
A new method combining correlation based clustering and rough sets attribute reduction together for gene selection from gene expression data is proposed. Correlation based clustering is used as a filter to eliminate the redundant attributes, then the minimal reduct of the filtered attribute set is reduced by rough sets . Three different classification algorithms are employed to evaluate the performance...
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