Multi-view　clustering　leverages　diverse　information　sources　for　unsupervised　clustering.　While　existing　methods　primarily　focus　on　learning　a　fused　representation　matrix,　they　often　overlook　the　impact　of　private　information　and　noise.　To　overcome　this　limitation,　we　propose　a　novel　approach,　the　Multi-view　Semantic　Consistency　based　Information　Bottleneck　for　Clustering　(MSCIB).　Our　method　emphasizes　semantic　consistency　to　enhance　the　information　bottleneck　learning　process　across　different　views.　It　aligns　multiple　views　in　the　semantic　space,　capturing　valuable　consistent　information　from　multi-view　data.　The　learned　semantic　consistency　improves　the　ability　of　the　information　bottleneck　to　precisely　distinguish　consistent　information,　resulting　in　a　more　discriminative　and　unified　feature　representation　for　clustering.　Experimental　results　on　diverse　multi-view　datasets　demonstrate　that　MSCIB　achieves　state-of-the-art　performance.　In　comparison　with　the　average　performance　of　the　other　contrast　algorithms,　our　approach　exhibits　a　notable　improvement　of　at　least　4%.　©　2024