Granular-Ball Reliability Learning: Adaptive Splitting and Risk-Calibrated Connection for Deep Multi-View Clustering

Deep multi-view clustering increasingly learns representations, sample relations, and granular structures to integrate complementary information across views. However, fixing mixed local granular balls entrenches within-granular ball errors, while incorrectly merging pure granular balls amplifies between-granular ball errors. We propose GBASC, which combines Granular-Ball Adaptive Splitting (GBAS) to refine mixed local granular balls using cross-view evidence with Granular-Ball Adaptive Connection (GBAC) to determine global assignments from reliable between-granular ball associations. Our contributions are an exact decomposition of cross-granularity pairwise errors into within-atom mixing and between-atom assignment errors, and a unified framework that jointly controls both transitions through adaptive refinement, risk-aware partition selection, and reliability-weighted representation learning. GBASC achieves state-of-the-art clustering performance on six benchmark datasets.