Fuzzy　C-means　(FCM)　clustering　algorithm　tries　to　get　the　memberships　of　each　sample　to　each　Cluster　by　optimizing　an　objective　function,　and　then　assign　each　of　the　samples　to　an　appropriate　class.　The　Fuzzy　C-means　algorithm　doesn＇t　fit　for　clusters　with　different　sizes　and　different　densities,　and　it　is　sensitive　to　noise　and　anomaly.　We　present　two　improved　fuzzy　c-means　algorithms,　Clusters-Independent　Relative　Density　Weights　based　Fuzzy　C-means　(CIRDWFCM)　and　Clusters-Dependent　Relative　Density　Weights　based　Fuzzy　C-means　(CDRDWFCM),　according　to　the　various　roles　of　different　samples　in　clustering.　Several　experiments　of　them　are　done　on　four　datasets　from　UCI　and　UCR.　Experimental　results　shows　that　this　two　presented　algorithms　can　increase　the　similarity　or　decrease　the　iterations　to　some　extent,　and　get　better　clustering　results　and　improve　the　clustering　quality.　©　2010　Springer-Verlag　Berlin　Heidelberg.