A Hough Transform-Based Biclustering Algorithm for Gene Expression Data

Cuong To, Tien Thanh Nguyen, Alan Wee-Chung Liew

Abstract: In pattern classification, when the feature space is of high dimensionality or patterns are “similar” on a subset of features only, the traditional clustering methods do not show good performance. Biclustering is a class of methods that simultaneously carry out grouping on two dimensions and has many applications to different fields, especially gene expression data analysis. Because of simultaneous classification on both rows and columns of a data matrix, the biclustering problem is inherently intractable and computationally complex. One of the most complex models in biclustering problem is linear coherent model. Several biclustering algorithms based on this model have been proposed in recent years. However, none of them is able to perfectly recognize all linear patterns in a bicluster. In this work, we propose a novel algorithm based on Hough transform that can find all linear coherent patterns. In the sequel we apply it to gene expression data.

External IDs:dblp:conf/icmlc/ToNL14