A Novel Clustering and Verification Based Microarray Data Bi-clustering Method

Microarray data biclustering is very important for the research on gene regulatory mechanisms. Genes which exhibit similar patterns are often functionally related. In this paper a novel bicluster detection method is proposed. It makes use of one of the existing traditional clustering algorithms such as K-means as an intermediate tool to do data clustering with the submatrices created from the original data matrix. Especially, in order to save the memory storage requirement, reduce the useless clustering processing and accelerate the bicluster detection speed, a clustering and verification combined algorithm is applied. The former helps to find out the row numbers where possible biclusters lie in, while the latter efficiently speed up the detection processing. Based on a characteristic of bicluster, the biclusters are detected one by one. At the end of the paper experiment with the simulated data are presented.

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