  Incomplete multi-view clustering is a challenging and non-trivial task to
provide effective data analysis for large amounts of unlabeled data in the real
world. All incomplete multi-view clustering methods need to address the problem
of how to reduce the impact of missing views. To address this issue, we propose
diffusion completion to recover the missing views integrated into an incomplete
multi-view clustering framework. Based on the observable views information, the
diffusion model is used to recover the missing views, and then the consistency
information of the multi-view data is learned by contrastive learning to
improve the performance of multi-view clustering. To the best of our knowledge,
this may be the first work to incorporate diffusion models into an incomplete
multi-view clustering framework. Experimental results show that the proposed
method performs well in recovering the missing views while achieving superior
clustering performance compared to state-of-the-art methods.
