In　recent　years,　the　problem,　which　copes　with　high-dimensional　data　by　the　method　of　cluster　analysis,　has　become　a　focus　and　difficulty　in　the　field　of　artificial　intelligence.　Many　conventional　soft　subspace　clustering　techniques　merge　several　criteria　into　a　single　objective　to　improve　performance,　however,　the　weighting　parameters　become　important　but　difficult　to　set.　A　novel　soft　subspace　clustering　with　a　multi-objective　evolutionary　approach　(MOSSC)　is　proposed　to　this　problem.　First,　two　new　objective　function　is　constructed　by　minimizing　the　within-cluster　compactness　and　maximizing　the　between-cluster　separation　based　on　the　framework　of　soft　subspace　clustering　algorithm.　Based　on　this　objective　function,　a　new　way　of　computing　clusters’　feature　weights,　centers　and　membership　is　then　derived　by　using　Lagrange　multiplier　method.　The　properties　of　this　algorithm　are　investigated　and　the　performance　is　evaluated　experimentally　using　UCI　datasets.　©　2018　Association　for　Computing　Machinery.