A Parameter-Free Multi-View Clustering Framework With Adaptive Anchors for Large-Scale Data

Anchor-based multi-view clustering has gained increasing attention for its efficiency in approximating similarity structures and scaling to large datasets. To reduce the burden of manual hyper-parameter tuning, recent studies have introduced parameter-free extensions. However, existing methods still face critical challenges: anchors are typically fixed after initialization, limiting adaptability to heterogeneous data; enforcing a shared anchor set across views suppresses view-specific diversity; and heuristic or self-weighted fusion strategies often lack explicit cross-view alignment, resulting in structural inconsistencies. To address these issues, we propose a Parameter-Free Multi-view Clustering framework with Adaptive Anchors for Large-scale Data (FPMCAA). Unlike existing approaches that decouple anchor construction and graph fusion, FPMCAA integrates adaptive anchor learning, anchor graph construction, and explicit cross-view alignment within a unified optimization model. Anchors are iteratively refined to capture complex distributions, while view-specific graphs are aligned toward a consensus structure without sacrificing inherent diversity. The framework avoids manual hyperparameter tuning and achieves linear computational complexity through efficient alternating optimization. Extensive experiments on benchmark datasets demonstrate that FPMCAA consistently outperforms state-of-the-art methods in clustering performance, robustness, and scalability. The source code of FPMCAA is available athttps://github.com/Xuchen2020/FPMCAA.