Dual-Reliable Contrastive Fusion for Multi-View Clustering

Multi-view clustering (MVC) has garnered significant attention in recent years due to its ability to leverage shared information across heterogeneous data sources. However, most existing methods focus on improving clustering performance while often neglecting potential conflicts between views and the uncertainty in view distributions. To address this limitation, we propose a novel framework named dual-reliable contrastive fusion multi-view clustering (DRCFMVC). This framework organically integrates uncertainty and conflict within a multi-view contrastive clustering paradigm for the first time. Through an innovative dual reliability weighting mechanism, uncertainty and conflict are systematically incorporated into contrastive learning. Specifically, high-dimensional features are first mapped to cluster distributions via a clustering network. Subsequently, Dempster–Shafer Theory (DST) estimates prediction uncertainty within each view, while Jensen–Shannon (JS) divergence quantifies conflict levels between views. These two complementary types of information are further integrated through the proposed dual-reliable weighting (DRW) fusion strategy, effectively guiding the contrastive optimization process to learn more stable and discriminative representations. Extensive experiments on eleven public benchmark datasets demonstrate that the proposed method achieves superior performance across multiple clustering evaluation metrics compared to existing state-of-the-art approaches. The source code can be made accessible at https://github.com/li-zi-qi/DRCFMVC.