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.
https://hal.archives-ouvertes.fr/hal-01306473 Oct 2015, Melbourne, Australia. 2015, CIKM '15 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 978-1-4503-3794-6. <10.1145/2806416.2806639>ARRAY(0x7f3e7b62d120) 2015-10-23