An improved co-similarity measure for document clustering

Abstract—Co-clustering has been defined as a way to or-ganize simultaneously subsets of instances and subsets of features in order to improve the clustering of both of them. In previous work [1], we proposed an efficient co-similarity measure allowing to simultaneously compute two similarity matrices between objects and features, each built on the basis of the other. Here we propose a generalization of this approach by introducing a notion of pseudo-norm and a pruning algorithm. Our experiments show that this new algorithm significantly im-proves the accuracy of the results when using either supervised or unsupervised feature selection data and that it outperforms other algorithms on various corpora. Keywords-co-clustering; similarity measure; text mining I