Mining constant row bicluster in gene expression data
Wang Miao · Jisuanji yingyong yanjiu · 2011
Biclustering is one of important techniques for gene expression data analysis.A bicluster is a set of genes cohe-rently expressed for a set of biological conditions.Various biclustering algorithms have been proposed to find biclusters of different types.However,most of them are not efficient.This paper proposed a novel algorithm MRCluster to mine constant row biclusters from real-valued dataset.MRCluster used Apriori property and several novel pruning techniques to mine biclusters efficiently.This paper compared the proposed algorithm with a recent approach RAP.The experimental results show that MRCluster is much more efficient than RAP in mining biclusters with constant rows.As a result,MRCluster can efficiently find out constant row bicluster from real-valued gene expression data.