In　the　era　of　big　data,　the　ability　of　humans　to　collect,　store,　transmit　and　manage　data　has　been　increasingly　improved.　Various　industries　have　accumulated　a　large　amount　of　data　resources,　which　are　often　multi-source　and　heterogeneous.　How　to　effectively　cluster　these　multi-source　data　(also　known　as　multi-view　clustering)　has　become　one　of　the　focuses　of　today＇s　machine　learning　research.　The　existing　multi-view　clustering　algorithms　mainly　pay　attention　to　the　contribution　of　different　views　and　features　to　the　cluster　structure　from　the　＇global＇　perspective,　without　considering　the　＇local＇　information　complementary　differences　between　different　samples.　Therefore,　this　paper　proposes　a　new　sample-weighted　multi-view　clustering　(SWMVC).　The　method　weights　each　sample　with　different　views　and　adopts　alternating　direction　method　of　multipliers　(ADMM)　to　learn　sample　weight,　which　can　not　only　learn　the　＇local＇　difference　of　weights　among　multiple　views　in　different　sample　points,　but　also　reflect　the　＇global＇　difference　of　the　contribution　of　different　views　to　the　cluster　structure,　and　has　better　flexibility.　Experiments　on　multiple　datasets　show　that　the　SWMVC　method　has　a　better　clustering　effect　on　heterogeneous　view　data.　©　2019,　Science　Press.　All　right　reserved.