Non-Negative Matrix Tri-Factorization for co-clustering: an analysis of the block matrix

Non-negative dyadic data, that is data representing observations which relate two finite sets of objects, appear in several domain applications, such as text-mining-based information retrieval, collaborative filtering and recom- mender systems, micro-array analysis and computer vision. Discovering la- tent subgroups among data is a fundamental task to be performed on dyadic data. In this context, clustering and co-clustering techniques are relevant tools for extracting and representing latent information in high dimensional data. Recently, Non-negative Matrix Factorizations attracted a great interest as clustering methods, due to their capability of performing a parts-based de- composition of data. In this paper, we focus our attention on how NMF with additional constraints can be properly applied for co-clustering non-negative dyadic data. In particular, we present a process which aims at enhancing the performance of 3-factors NMF as a co-clustering method, by identifying a clearer correlation structure represented by the block matrix. Experimental evaluation performed on some common datasets, by applying the proposed approach on two different NMF algorithms, shows that, in most cases, the quality of the obtained clustering increases, especially in terms of average inter-cluster similarity.