In　the　spherical　k-means　problem　(SKMP),　which　is　a　well-studied　clustering　problem　in　text　mining,　we　are　given　an　n-point　set　D　in　d-dimensional　unit　sphere　Sd,　and　an　integer　k　＜=　n.　The　goal　is　to　find　a　center　subset　S　c　Sd　with　vertical　bar　k　vertical　bar　＜=　k　that　minimizes　the　sum　of　cosine　dissimilarity　measure　for　each　point　in　D　to　the　nearest　center.　We　prove　that　any　gamma-approximation　algorithm　for　the　k-means　problem　(KMP)　can　be　adapted　to　the　SKMP　with　2　gamma-approximation　ratio.　It　follows　that　there　is　a　local　search　(18　+　c)-approximation　algorithm　for　the　SKMP,　by　leveraging　the　classical　local　search　(9　+　c)-approximation　algorithm　for　the　KMP.　Therefore,　an　interesting　problem　arises,　that　is　whether　there　exists　an　approximation　algorithm　using　local　search　scheme　directly　for　the　SKMP.　In　this　paper,　we　present　a　local　search　approximation　algorithm　for　the　SKMP　and　prove　its　performance　guarantee　is　(2(4　+　root　7)　+　c).　We　also　conduct　numerical　computation　to　show　the　efficiency　of　the　local　search　approximation　algorithm　by　single-swap　operation　in　the　end.　(C)　2020　Elsevier　B.V.　All　rights　reserved.