K-means　algorithm　is　a　well-known　clustering　method.　Typically,　the　k-means　algorithm　treats　all　features　fairly　and　sets　weights　of　all　features　equally　when　evaluating　dissimilarity.　However,　experiment　results　show　that　a　meaningful　clustering　phenomenon　often　occurs　in　a　subspace　defined　by　some　specific　features.　Different　dimensions　make　contributions　to　the　identification　of　points　in　a　cluster.　The　contribution　of　a　dimension　is　represented　as　a　weight　that　can　be　treated　as　the　degree　of　the　dimension　in　contribution　to　the　cluster.　This　paper　first　proposes　Weight　in　Competitive　K-means　(WCKM).　which　derives　from　Improved　K-means　and　Entropy　Weighting　K-means.　By　adding　weights　to　the　objective　function,　the　contributions　from,　each　feature　of　each　clustering　could　simultaneously　minimize　the　dispersion　within　clusters　and　maximize　the　separation　between　clusters.　The　proposed　algorithm　is　confirmed　by　experiments　on　real　data　sets.