Preserving Consistency and Progressively Integrating Diversity for Continual Multi-View Clustering

Continual multi-view clustering (CMVC) updates clustering representations as new views arrive sequentially, requiring both the preservation of shared structures and the integration of complementary information. Existing methods typically preserve historical knowledge by constraining current representations to remain compatible with previous ones. Such compatibility constraints provide limited semantic guidance and often underexploit view-specific diversity. To address these limitations, we propose Preserving Consistency and Progressively Integrating Diversity for Continual Multi-View Clustering (PC-PID). PC-PID decomposes adjacent-view representations into a shared consistency component and view-specific diversity components. Contrastive knowledge transfer strengthens semantic dependence between current and historical consistency representations while suppressing redundant dependence between adjacent diversity representations, thereby preserving shared structures and separating view-specific information. Progressive tensor fusion constructs a local tensor from adjacent diversity representations and captures their high-order correlations as new views arrive. These two mechanisms are incorporated into a unified objective and optimized through an alternating algorithm. Experiments on eight benchmark datasets against 14 representative static and continual multi-view clustering methods demonstrate the effectiveness and efficiency of PC-PID.