Auto-weighted Graph Reconstruction for efficient ensemble clustering

Abstract

Ensemble clustering (EC) is an important field in data clustering, which aims to combine multiple base clustering results of a given dataset into a single consensus result. Previous studies have shown that one of the effective methods is to convert the base clustering results into a graph partitioning problem. Existing spectral ensemble clustering methods typically obtain a spectral embedding matrix for the graph and then perform k-means, separating the graph construction and clustering tasks into two distinct stages. To address these challenges, we propose a novel and efficient ensemble clustering method named Auto-weighted Graph Reconstruction for efficient ensemble clustering. This method integrates weighted base clusters, consensus distance graph construction, and clustering into a unified framework. A consensus distance graph is constructed, from which a stable and discrete label matrix is derived using spectral clustering. An effective optimization algorithm is then employed to address the resulting problem. Finally, the experimental results on eight real-world datasets verify the effectiveness and superiority of the proposed method.