An approximation ratio for biclustering

Kai Puolamäki, Sami Hanhijärvi, Gemma C. Garriga

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 + 2 under L 1 -norm for 0–1 valued matrices, and of 2 under L 2 -norm for real valued matrices.