With　the　popularity　of　cameras　and　sensors,　massive　data　are　captured　from　various　view　angles　or　modalities,　which　provide　abundant　complementary　information　and　also　bring　great　challenges　for　traditional　clustering　methods.　In　this　article,　we　propose　a　novel　Adaptive　K-Multiple-Means　for　multi-view　clustering　method　(AKM(3)C).　Unlike　traditional　multi-view　K-means　methods　by　grouping　samples　into　C　clusters　each　with　a　cluster　center　in　every　view,　the　proposed　AKM(3)C　employs　M　(M＞C)　sub-cluster　centers　in　each　view　to　reveal　the　sub-cluster　structure　in　the　multi-view　data　thus　enhances　the　clustering　performance.　Additionally,　to　distinguish　the　importance　of　different　views,　instead　of　using　empirical　weights,　AKM(3)C　exploits　the　multi-view　combination　weights　strategy　to　assign　a　weight　to　each　view　automatically　and　thus　fuses　the　complementary　information　of　different　views　properly　to　get　an　optimally　shared　bipartite　graph,　on　which　the　Laplacian　rank　constraint　is　executed　and　the　final　clusters　are　obtained　by　directly　partitioning.　An　efficient　optimization　algorithm　proposed　with　complexity　and　convergence　analysis　is　used　to　solve　the　proposed　AKM(3)C　method.　The　extensive　experimental　results　on　eight　public　datasets　show　that　the　proposed　AKM(3)C　performs　better　than　state-of-the-art　multi-view　clustering　methods.　The　code　can　be　downloaded　at　https://drive.google.com/file/d/1CQ0royrYxKFJdNLnbBQSbDrohtfH71di/view?usp=sharing.