PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

An Approximation Ratio for Biclustering
Kai Puolamäki, Sami Hanhijärvi and Gemma Garriga
Information Processing Letters Volume 108, Number 2, pp. 45-49, 2008.


The problem of biclustering consists of the simultaneous clustering of rows and columns of a matrix such that each of the submatrices induced by a pair of row and column clusters is as uniform as possible. In this paper we approximate the optimal biclustering by applying one-way clustering algorithms independently on the rows and on the columns of the input matrix. We show that such a solution yields a worst-case approximation ratio of 1+sqrt(2) under L1-norm for 0-1 valued matrices, and of 2 under L2-norm for real valued matrices.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:3418
Deposited By:Kai Puolamäki
Deposited On:10 February 2008