Deep contrastive multi-view clustering aims to use contrastive mechanisms to exploit the complementary information from multiple features, which has attracted increasing attention in recent years. However, we observe that most contrastive multi-view clustering methods neglect the false sample pairs caused by hard samples during the process of constructing contrastive sample pairs, including negative samples exhibit high similarity and positive samples exhibit low similarity. To address this problems, we propose a novel deep contrastive multi-view clustering network for hard sample mining, term...
