Relationship Alignment for View-aware Multi-view Clustering

Multi-view clustering improves clustering performance by integrating complementary information from multiple views. However, existing methods often suffer from two limitations: i) the neglect of preserving sample neighborhood structures, which weakens the consistency of inter-sample relationships across views; and ii) inability to adaptively utilize inter-view similarity, resulting in representation conflicts and semantic degradation. To address these issues, we propose a novel framework named Relationship Alignment for View-aware Multi-view Clustering (RAV). Our approach first constructs a sample relationship matrix based on the deep features of each view and aligns it with the global relationship matrix to enhance neighborhood consistency across views and facilitate the accurate measurement of inter-view similarity. Simultaneously, we introduce a view-aware adaptive weighting mechanism for label contrastive learning. This mechanism dynamically adjusts the contrastive intensity between view pairs based on the similarity of their deep features: higher-similarity views lead to stronger label alignment, while lower-similarity views reduce the weighting to prevent forcing inconsistent views into agreement. This strategy effectively promotes cluster-level semantic consistency while preserving natural inter-view relationships. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art approaches on multiple benchmark datasets.