关键词: 多视图聚类, 二部图, 一致图, 图融合, 嵌入学习

Abstract: The effectiveness and applicability of multi-view clustering algorithms for multi-view data processing required further improvement. Most existing multi-view clustering algorithms were hindered by incomplete fusion mechanisms, insufficient exploration of multi-view collaborative relationships, and unstable robustness. These limitations led to lower consistency in clustering outcomes and reduced stability when handling noise and redundant information. To address these challenges, a novel Multi-View Clustering Algorithm Based on Bipartite Graph and Consistency Graph Learning (BGC-MVC) was developed to enhance clustering consistency and complementarity by effectively integrating information from multiple views. A bipartite graph was first constructed to capture the neighborhood relationships between different views. Subsequently, a consensus graph was learned to strengthen inter-view similarity. The embeddings of the original multi-view data were integrated into a unified framework, combining graph learning with the clustering process to improve overall clustering performance. Experimental results demonstrated that significant improvements in evaluation metrics were achieved under convergence conditions. On the MSRC_v1 dataset, the F-score increased by 19.84 percentage points compared to the second best LMVSC algorithm. Furthermore, BGC-MVC exhibited superior robustness and accuracy.

Key words: multi-view clustering, bipartite graph, consensus graph, graph fusion, embedding learning