In　the　existing　subspace　clustering　algorithms,　it　is　assumed　that　the　data　is　derived　from　a　union　of　multiple　linear　subspace,　and　these　algorithms　cannot　deal　with　problems　of　nonlinear　and　time　warping　in　time　series　clustering.　To　overcome　these　issues,　elastic　kernel　low　rank　representation　subspace　clustering(EKLRR)　and　elastic　kernel　least　squares　regression　subspace　clustering(EKLSR)　are　proposed　by　introducing　kernel　tricks　and　elastic　distance,　and　they　are　called　elastic　kernel　subspace　clustering(EKSC).　Moreover,　the　grouping　effect　of　EKLSR　and　the　convergence　of　EKLRR　are　proved　theoretically.　The　experimental　results　on　five　UCR　datasets　show　the　effectiveness　of　the　proposed　algorithms.　©　2017,　Science　Press.　All　right　reserved.