Co-clustering Document-term Matrices by Direct Maximization of Graph Modularity

Abstract

We present Coclus, a novel diagonal co-clustering algorithm which is able to effectively co-cluster binary or contingency matrices by directly maximizing an adapted version of the modularity measure traditionally used for networks. While some effective co-clustering algorithms already exist that use network-related measures (normalized cut, modularity), they do so by using spectral relaxations of the discrete optimization problems. In contrast, Coclus allows to get even better co-clusters by directly maximizing modularity using an iterative alternating optimization procedure. Extensive comparative experiments performed on various document-term datasets demonstrate that our algorithm is very effective, stable and outperforms other co-clustering algorithms.