The　well-known　Fuzzy　C-Means　(FCM)　algorithm　and　its　modified　clustering　derivatives　have　been　widely　applied　in　various　fields.　However,　previous　studies　have　focused　on　the　yield　of　correctly　clustered　data,　and　few　have　addressed　the　alignment　of　extracted　influential　areas　of　clusters　to　natural　cluster　structure.　Various　clustering　algorithms　present　diverse　characteristics　in　cluster　structure　detection　due　to　the　different　clustering　principles　involved.　For　example,　Mahalanobis　distance-based　FCM　algorithms　effectively　detect　the　influential　direction　of　each　cluster,　while　kernel-based　FCM　algorithms　provide　an　interface　for　adjusting　the　influential　range.　Combining　the　advantages　of　these　previous　algorithms,　the　Adaptive　Kernel　Fuzzy　C-Means　(AKFCM)　algorithm　based　on　cluster　structure　is　proposed　in　this　paper.　The　AKFCM　algorithm　can　effectively　detect　the　influential　direction　and　adjust　the　influential　range　of　each　cluster　with　adaptive　kernelization.　By　applying　the　previous　and　AKFCM　algorithms　to　both　synthetic　and　real-world　datasets,　the　proposed　algorithm　is　proven　to　achieve　better　performance　not　only　in　clustering　accuracy　but　also　in　the　extraction　of　reasonable　influential　areas.　The　proposed　algorithm　could　be　helpful　for　clustering　datasets　composed　of　clusters　with　different　directions　and　ranges　in　structure.