We　consider　the　spherical　k-means　problem　(SKMP),　a　generalization　of　the　k-means　clustering　problem　(KMP).　Given　a　data　set　of　n　points　(Formula　Presented)　in　d-dimensional　unit　sphere　(Formula　Presented),　and　an　integer　(Formula　Presented),　it　aims　to　partition　the　data　set　(Formula　Presented)　into　k　sets　so　as　to　minimize　the　sum　of　cosine　dissimilarity　measure　from　each　data　point　to　its　closest　center.　We　present　a　constant　expected　approximation　guarantee　for　this　problem　based　on　integrating　the　k-means++　seeding　algorithm　for　the　KMP　and　the　local　search　technique.　©　2020,　Springer　Nature　Switzerland　AG.