Ensemble clustering via fusing global and local structure information

Ensemble clustering is aimed at obtaining a robust consensus result from a set of weak base clusterings. Most existing methods rely on a co-association (CA) matrix that describes the frequency at which pairs of samples are clustered into the same class and exhibits a symmetrical property. However, these methods typically focus on either the global or local structure of the CA matrix and do not consider using both types of information simultaneously to improve ensemble clustering performance. To address this issue, we propose a novel scheme to Fuse both the global Structure and the local structure information for Ensemble Clustering, namely FSEC, in this paper. Specifically, FSEC integrates both global structural information and local structural information into a learning framework by self-expressive and CA matrix self-enhancement models respectively. Moreover, FSEC embeds a Hadamard product fusion term to maximize the commonalities between global structure and local structure. The objective function optimization problem is solved by using the alternating direction method of multipliers (ADMM). Experimental results demonstrate that the proposed FSEC outperforms many state-of-the-art methods of ensemble clustering.

Automated summary

This paper proposes a novel scheme called FSEC to improve the performance of ensemble clustering by integrating both global and local structural information into a learning framework. Experimental results demonstrate that FSEC outperforms other state-of-the-art methods of ensemble clustering.