Incomplete multi-view clustering (IMVC) aims to partition unlabeled multi-view data into semantically coherent groups, even when certain views are missing due to sensor failures, data collection constraints, or privacy concerns. Despite advancements in deep IMVC methods, two critical challenges remain unresolved: (i) the lack of explicit mechanisms to model cross-view complementarity and (ii) the absence of principled strategies to ensure global semantic consistency across views. To address these challenges, we propose SP-IMVC, a novel Synergistic Prompting framework that jointly models complementarity and consistency under view incompleteness. Specifically, we introduce two types of learnable prompts: the Cross-View Complementary Prompt (CVCP), which aggregates auxiliary representations from available views to enrich the semantics of the current view and mitigate information loss; and the Latent Anchor Prompt (LAP), which utilizes a global anchor prompt pool to provide adaptive semantic priors that promote globally consistent representations. These prompts are optimized jointly within a unified architecture to achieve synergistic prompting of cross-view complementarity and global semantic consistency. Extensive experiments on six public benchmarks demonstrate that SP-IMVC consistently outperforms 14 state-of-the-art IMVC approaches, particularly in scenarios with high missing-view ratios, validating the effectiveness and robustness of our synergistic prompt-guided clustering framework. The code will be released to facilitate future research.
Hao et al. (Thu,) studied this question.