Clustering　nonlinearly　separable　datasets　is　always　an　important　problem　in　unsupervised　machine　learning.　Graph　cut　models　provide　good　clustering　results　for　nonlinearly　separable　datasets,　but　solving　graph　cut　models　is　an　NP　hard　problem.　A　novel　graph-based　clustering　algorithm　is　proposed　for　nonlinearly　separable　datasets.　The　proposed　method　solves　the　min　cut　model　by　iteratively　computing　only　one　simple　formula.　Experimental　results　on　synthetic　and　benchmark　datasets　indicate　the　potential　of　the　proposed　method,　which　is　able　to　cluster　nonlinearly　separable　datasets　with　less　running　time.