Gene　expression　data　clustering　offers　a　powerful　approach　to　detect　cancers.　Specifically,　gene　expression　data　clustering　based　on　nonnegative　matrix　factorization　(NMF)　has　been　widely　applied　to　identify　tumors.　However,　traditional　NMF　methods　cannot　deal　with　negative　data　and　easily　lead　to　local　optimum　because　the　iterative　methods　are　adopted　to　solve　the　optimal　problem.　To　avoid　these　problems　of　NMF　methods,　we　propose　graph　regularized　subspace　segmentation　method　(GRSS)　for　clustering　gene　expression　data.　The　global　optimal　solution　of　GRSS　can　be　achieved　by　solving　a　Sylvester　equation.　Experimental　results　on　eight　gene　expression　data　sets　show　that　GRSS　has　significant　performance　improvement　compared　with　other　subspace　segmentation　methods,　traditional　clustering　methods　and　various　extensions　of　NMF.　©　2014　Elsevier　B.V.