On approximate balanced bi-clustering

Authors:
Guoxuan Ma;Jiming Peng;Yu Wei
Affiliations:
Department of Computing and Software, McMaster University, Hamilton, Ontario, Canada;Department of Computing and Software, McMaster University, Hamilton, Ontario, Canada;Department of Computing and Software, McMaster University, Hamilton, Ontario, Canada
Venue:
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
Year:
2005

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Abstract

We consider the balanced bi-clustering problem for a given data set, where the number of entities in each cluster is bounded, and its special case where the number of entities in each cluster is fixed. Several algorithms to attack these problems are proposed. In particular, a novel and efficient heuristic, in which we first reformulate the constrained bi-clustering problem into a quadratic programming(QP) problem and then try to solve it by optimization technique, is proposed. We prove that our algorithm can provide a 2-approximate solution to the original problem. Promising numerical results are reported.