Gene　expression　data　is　a　kind　of　high　dimension　and　small　sample　size　data.　The　clustering　accuracy　of　conventional　clustering　techniques　is　lower　on　gene　expression　data　due　to　its　high　dimension.　Because　some　subspace　segmentation　approaches　can　be　better　applied　in　the　high-dimensional　space,　three　new　subspace　clustering　models　for　gene　expression　data　sets　are　proposed　in　this　work.　The　proposed　projection　subspace　clustering　models　have　projection　sparse　subspace　clustering,　projection　low-rank　representation　subspace　clustering　and　projection　least-squares　regression　subspace　clustering　which　combine　projection　technique　with　sparse　subspace　clustering,　low-rank　representation　and　least-square　regression,　respectively.　In　order　to　compute　the　inner　product　in　the　high-dimensional　space,　the　kernel　function　is　used　to　the　projection　subspace　clustering　models.　The　experimental　results　on　six　gene　expression　data　sets　show　these　models　are　effective.　©　The　Author(s)　2017.