Multi-view　clustering　learns　consistent　information　from　multi-view　data,　aiming　to　achieve　more　significant　clustering　characteristics.　However,　data　in　real-world　scenarios　often　exhibit　temporal　or　spatial　asynchrony,　leading　to　views　with　unaligned　instances.　Existing　methods　primarily　address　this　issue　by　learning　transformation　matrices　to　align　unaligned　instances,　but　this　process　of　learning　differentiable　transformation　matrices　is　cumbersome.　To　address　the　challenge　of　partially　unaligned　instances,　we　propose　Partially　Multi-view　Clustering　via　Re-alignment　(PMVCR).　Our　approach　integrates　representation　learning　and　data　alignment　through　a　two-stage　training　and　a　re-alignment　process.　Specifically,　our　training　process　consists　of　three　stages:　(i)　In　the　coarse-grained　alignment　stage,　we　construct　negative　instance　pairs　for　unaligned　instances　and　utilize　contrastive　learning　to　preliminarily　learn　the　view　representations　of　the　instances.　(ii)　In　the　re-alignment　stage,　we　match　unaligned　instances　based　on　the　similarity　of　their　view　representations,　aligning　them　with　the　primary　view.　(iii)　In　the　fine-grained　alignment　stage,　we　further　enhance　the　discriminative　power　of　the　view　representations　and　the　model＇s　ability　to　differentiate　between　clusters.　Compared　to　existing　models,　our　method　effectively　leverages　information　between　unaligned　samples　and　enhances　model　generalization　by　constructing　negative　instance　pairs.　Clustering　experiments　on　several　popular　multi-view　datasets　demonstrate　the　effectiveness　and　superiority　of　our　method.　Our　code　is　publicly　available　at　https://github.com/WenB777/PMVCR.git.　©　2024　Elsevier　Ltd