Concept mining of binary gene expression data

Abstract

In this paper, we address the concept mining of binary gene expression data. To deal with this problem, we first compute the left and right singular vector matrices from the input binary gene expression matrix, and then information entropy is employed to determine whether column-clustering or row-clustering is performed first. Finally, the column-clustering and the row-clustering are repeated iteratively until the stopping criterion is satisfied. Experimental results show that our algorithm can identify the non-overlapping biclusters effectively. © 2014 Springer International Publishing Switzerland.