Rethinking Multi-View Clustering through Semantic Recomposition

Multi-view clustering (MVC) seeks to discover meaningful groups by integrating complementary descriptions of the same samples. However, geometric consistency alone does not ensure semantic correctness: nuisance variation can fragment one class across clusters, while shared contextual cues can mix distinct classes. Correcting these errors calls for semantic evidence that reveals both shared identities across clusters and distinct identities within them. We propose Semantic Recomposition (SemReC), which starts from a geometric partition, extracts semantic descriptions from sparse, coverage-oriented representatives using a multimodal large language model, and organizes them into frozen semantic seeds. These seeds guide split proposals, while a joint geometric–semantic objective evaluates coupled merge–split moves that preserve the cluster count. Across four benchmarks spanning visual, image–text, and audio–text settings, SemReC achieves an average ACC of 84.97% and the highest ACC on all four datasets among the compared baselines.