An Approximation Ratio for Biclustering
Data Structures and Algorithms
2008-08-22 v2 Machine Learning
Abstract
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.
Cite
@article{arxiv.0712.2682,
title = {An Approximation Ratio for Biclustering},
author = {Kai Puolamäki and Sami Hanhijärvi and Gemma C. Garriga},
journal= {arXiv preprint arXiv:0712.2682},
year = {2008}
}
Comments
9 pages, 2 figures; presentation clarified, replaced to match the version to be published in IPL