In　this　paper,　we　study　the　spherical　k-means　problem　(SKMP)　which　is　one　of　the　most　well-studied　clustering　problems.　In　the　SKMP,　we　are　given　an　n-client　set　D　in　d-dimensional　unit　sphere　S-d,　and　an　integer　k　＜=　n.　The　goal　is　to　open　a　center　subset　F　subset　of　S-d　with　vertical　bar　F　vertical　bar　＜=　k　that　minimizes　the　sum　of　cosine　dissimilarity　measure　for　each　client　in　D　to　the　nearest　open　center.　We　give　a　(2(4　+　root　7)　+　epsilon)-approximation　algorithm　for　this　problem　using　local　search　scheme.