Toward Scalable Multi-View Clustering Without Predefined Clusters via Inter-Anchor Graph Learning

Multi-view clustering aims to leverage complementary information from multiple data sources to improve clustering quality. Traditional graph-based Non-k multi-view clustering methods enable automatic cluster number determination but suffer from severe scalability issues due to their reliance on constructing large sample-level affinity graphs with quadratic complexity. To address this limitation, we propose a novel scalable Non-k Multi-View Clustering framework via Inter-Anchor Graph learning (MVC-IAG). Our method first extracts a small set of representative anchors via k-means on concatenated multi-view features, then learns a unified inter-anchor graph by integrating multi-view structural information and feature similarity priors. Our framework performs Non-k cluster discovery directly on this compact, learned inter-anchor graph, thereby enabling automatic cluster number determination, and subsequently propagates the results to all samples. Extensive experiments on multiple large scale datasets demonstrate that MVC-IAG significantly reduces computational cost while achieving competitive or superior clustering performance compared to state-of-the-art Non-k multi view clustering approaches.