Data　clustering　is　a　fundamental　topic　in　machine　learning　and　data　mining　areas.　In　recent　years,　researchers　have　proposed　a　series　of　effective　methods　based　on　Low　Rank　Representation　(LRR)　which　could　explore　low-dimension　subspace　structure　embedded　in　original　data　effectively.　The　traditional　LRR　methods　usually　are　designed　for　vectorial　data　from　linear　spaces　with　Euclidean　distance.　However,　high-dimension　data　(such　as　video　clip　or　imageset)　are　always　considered　as　non-linear　manifold　data　such　as　Grassmann　manifold　with　non-linear　metric.　In　addition,　traditional　LRR　clustering　method　always　adopt　single　nuclear　norm　as　low　rank　constraint　which　would　lead　to　suboptimal　solution　and　decrease　the　clustering　accuracy.　In　this　paper,　we　proposed　a　new　low　rank　method　on　Grassmann　manifold　for　video　or　imageset　data　clustering　task.　In　the　proposed　method,　video　or　imageset　data　are　formulated　as　sample　data　on　Grassmann　manifold　first.　And　then　a　double　low　rank　constraint　is　proposed　by　combining　the　nuclear　norm　and　bilinear　representation　for　better　construct　the　representation　matrix.　The　experimental　results　on　several　public　datasets　show　that　the　proposed　method　outperforms　the　state-of-the-art　clustering　methods.