In　this　paper,　we　study　the　sum　of　squares　facility　location　problem　(SOS-FLP)　which　is　an　important　variant　of　k-means　clustering.　In　the　SOS-FLP,　we　are　given　a　client　set　C　subset　of　R-p　and　a　uniform　center　opening　cost　f　＞　0.　The　goal　is　to　open　a　finite　center　subset　F　subset　of　R-p　and　to　connect　each　client　to　the　closest　open　center　such　that　the　total　cost　including　center　opening　cost　and　the　sum　of　squares　of　distances　is　minimized.　The　SOS-FLP　is　introduced　firstly　by　Bandyapadhyay　and　Varadarajan　(in:　Proceedings　of　SoCG　2016,　Article　No.　14,　pp　14:　1-14:　15,　2016)　which　present　a　PTAS　for　the　fixed　dimension　case.　Using　local　search　and　scaling　techniques,　we　offer　the　first　constant　approximation　algorithm　for　the　SOS-FLP　with　general　dimension.　We　further　consider　the　discrete　version　of　SOS-FLP,　in　which　we　are　given　a　finite　candidate　center　set　with　nonuniform　opening　cost　comparing　with　the　aforementioned　(continue)　SOS-FLP.　By　exploring　the　structures　of　local　and　optimal　solutions,　we　claim　that　the　approximation　ratios　are　7.7721　+　is　an　element　of　and　9　+　is　an　element　of　for　the　continue　and　discrete　SOS-FLP　respectively.