An ensemble clustering method via learning the CA matrix with fuzzy neighbors

Abstract

Although existing studies have confirmed that ensemble clustering methods based on co-association (CA) have been widely employed successfully, they still have the following drawback: the clustering performance and stability of ensemble clustering results heavily depend on the CA matrix. To enhance clustering performance while maintaining the stability of ensemble clustering results, an ensemble clustering method via learning the CA matrix with fuzzy neighbors (EC–CA–FN) is proposed in this study. First, EC–CA–FN constructs an accurate CA matrix by using both intra-cluster and inter-cluster relationships of pairwise samples from all base clustering results. Second, to improve the stability of ensemble clustering results, EC–CA–FN introduces a fuzzy index and the rank constraints on the constructed accurate CA matrix. This method invents a new ensemble clustering framework that learns the optimal fuzzy CA (FCA) matrix by adaptively assigning fuzzy neighbors of samples, thus obtaining the optimal clustering structure. Third, an alternative optimization method and weighting mechanism are adopted to achieve the optimal FCA matrix and adaptively assign all base clustering results. The experimental results on all adopted datasets indicate the effectiveness of EC–CA–FN in terms of both clustering performance and the stability of ensemble clustering results.