A graph spectrum framework for optimizing the combination process of geometric biclustering

In microarray data, a bicluster refers to a subset of genes exhibiting consistent patterns over a subset of conditions. In this paper, we propose a method for detecting these biclusters in large gene expression datasets. We consider the bicluster patterns based geometric relations. We use Randomized Hough Transform for sub-bicluster detection in column pair spaces and a spectra graph based combination algorithm is formulated to reduce the time complexity for combining the sub-biclusters. Experiment results demonstrate that our approach reduces the computing time and outperforms existing biclustering algorithms with higher biclustering accuracy.