Ensemble Block Co-clustering: A Unified Framework for Text Data

Abstract

In this paper, we propose a unified framework for Ensemble Block Co-clustering (EBCO), which aims to fuse multiple basic co-clusterings into a consensus structured affinity matrix. Each co-clustering to be fused is obtained by applying a co-clustering method on the same document-term dataset. This fusion process reinforces the individual quality of the multiple basic data co-clusterings within a single consensus matrix. Besides, the proposed framework enables a completely unsupervised co-clustering where the number of co-clusters is automatically inferred based on the non trivial generalized modularity. We first define an explicit objective function which allows the joint learning of the basic co-clusterings aggregation and the consensus block co-clustering. Then, we show that EBCO generalizes the one side ensemble clustering to an ensemble block co-clustering context. We also establish theoretical equivalence to spectral co-clustering and weighted double spherical k-means clustering for textual data. Experimental results on various real-world document-term datasets demonstrate that EBCO is an efficient competitor to some state-of-the-art ensemble and co-clustering methods.