The BinOvNMTF Algorithm: Overlapping Columns Co-clustering Based on Non-negative Matrix Tri-factorization

Co-clustering is being given increasing attention by data scientists because it reveals a priori hidden information in data, through an analysis of item clusters along with attribute clusters. The use of co-clustering methods based on non-negative matrix factorization is considered to be advantageous for contexts in which data is positive matrices. However, there are limitations in these methods when co-clusters are characterized by columns overlapping (or attributes) – a common situation in several application contexts. In this paper, we have formalized the problem of Columns Overlapping Co-clustering and introduced BinOvNMTF (Binary Overlapped Non-negative Matrix Tri-Factorization), a new algorithm to analyze attribute clusters independently for each item cluster. This analysis is particularly useful for discovering information embedded in attribute clusters. We tested the BinOvNMTF algorithm in synthetic and real (textual) datasets; BinOvNMTF achieved superior results than those obtained by correlated algorithms.