Rethinking Multi-View Clustering as Symmetric Incremental Reasoning

Deep multi-view clustering seeks shared structure from heterogeneous observations without supervision; however, existing methods often rely on shallow view-level coupling, limiting progressive cross-view reasoning. We reformulate multi-view fusion as view-wise incremental reasoning and propose VIR-SCI, which infers complementary information for each view via anchor-excluded symmetric cross-view interactions. To improve discriminability and robustness, we further introduce feature-wise incremental reasoning to adaptively modulate cross-view information. Mutual-information-constrained semantic alignment and contrastive learning jointly preserve global–local semantic coherence throughout reasoning. Extensive experiments show that VIR-SCI consistently outperforms state-of-the-art methods.