FDCluster: Mining Frequent Closed Discriminative Bicluster without Candidate Maintenance in Multiple Microarray Datasets

Abstract

Biclustering is a methodology allowing for condition set and gene set points clustering simultaneously. Almost all the current biclustering algorithms find bicluster in one microarray dataset. In order to reduce the noise influence and find more biological biclusters, we propose an algorithm, FDCluster, to mine frequent closed discriminative bicluster in multiple microarray datasets. FDCluster uses Apriori property and several novel techniques for pruning to mine frequent closed bicluster without candidate maintenance. The experimental results show that FDCluster is more effectiveness than traditional method in either single micorarray dataset or multiple microarray datasets. We also test the biological significance using GO to show our proposed method is able to produce biologically relevant biclusters.