An Overview of Co-Clustering via Matrix Factorization

Abstract

Co-clustering algorithms have been widely used for text clustering and gene expression through matrix factorization. In recent years, diverse co-clustering algorithms which group data points and features synchronously have shown their advantages over traditional one-side clustering. In order to solve the co-clustering problems, most existing methods relaxed constraints via matrix factorization. In this paper, we provide a detailed understanding of six co-clustering algorithms with different performance and robustness. We conduct comprehensive experiments in eight real-world datasets to compare and evaluate these co-clustering methods based on four evaluation metrics including clustering accuracy, normalized mutual information, adjusted rand index, and purity. Our findings demonstrate the strengths and weaknesses of these methods and provide insights to motivate further exploration of co-clustering methods and matrix factorization.