A Structured Bipartite Graph Learning method for ensemble clustering

Given a set of base clustering results, conventional bipartite graph-based ensemble clustering methods typically require computing a sample-cluster similarity matrix from each base clustering result. These matrices are then either concatenated or averaged to form a bipartite weight matrix, which is used to create a bipartite graph. Graph-based partition techniques are subsequently applied to this graph to obtain the final clustering result. However, these methods often suffer from unreliable base clustering results, making it challenging to identify a clear cluster structure due to the variations in cluster structures across the base results. In this paper, we propose a novel Structured Bipartite Graph Learning (SBGL) method. Our approach begins by computing a sample-cluster similarity matrix from each base clustering result and constructing a base bipartite graph from each of these matrices. We assume these base bipartite graphs contain a set of latent clusters and project them into a set of sample-latent-cluster bipartite graphs. These new graphs are then ensembled into a bipartite graph with a distinct cluster structure, from which the final set of clusters is derived. Our method allows for different numbers of clusters across base clusterings, leading to improved performance. Experimental results on both synthetic and real-world datasets demonstrate the superior performance of our new method.