DSMVC: Disentangled Streaming Multi-View Clustering via Purification and Calibration

Streaming multi-view clustering has attracted growing attention for its ability to aggregate continuously arriving views without accessing historical raw data. However, emerging views often introduce private variations and unreliable cross-view correspondences, inevitably corrupting the global consensus over time. To address this issue, we propose a novel Disentangled Streaming Multi-View Clustering (DSMVC) framework. Specifically, DSMVC leverages spectral curvature to decouple dominant structures from private residuals, securing reliable knowledge accumulation through two core modules: an Attribution-Guided Suppression Module that suppresses vulnerable components by quantifying feature attribution sensitivity to purify residual representations, and a Discrepancy-Guided Cross-view Alignment Module that calibrates cross-view correspondences via permutation-induced prediction inconsistency, ensuring robust consensus semantics across streaming views. Extensive experiments across benchmark datasets demonstrate that DSMVC achieves substantial advancement over state-of-the-art methods, exhibiting remarkable robustness against continuous view arrivals.