Multiview　clustering　(MVC)　has　recently　received　great　interest　due　to　its　pleasing　efficacy　in　combining　the　abundant　and　complementary　information　to　improve　clustering　performance,　which　overcomes　the　drawbacks　of　view　limitation　existed　in　the　standard　single-view　clustering.　However,　the　existing　MVC　methods　are　mostly　designed　for　vectorial　data　from　linear　spaces　and,　thus,　are　not　suitable　for　multiple　dimensional　data　with　intrinsic　nonlinear　manifold　structures,　e.g.,　videos　or　image　sets.　Some　works　have　introduced　manifolds＇　representation　methods　of　data　into　MVC　and　obtained　considerable　improvements,　but　how　to　fuse　multiple　manifolds　efficiently　for　clustering　is　still　a　challenging　problem.　Particularly,　for　heterogeneous　manifolds,　it　is　an　entirely　new　problem.　In　this　article,　we　propose　to　represent　the　complicated　multiviews＇　data　as　heterogeneous　manifolds　and　a　fusion　framework　of　heterogeneous　manifolds　for　clustering.　Different　from　the　empirical　weighting　methods,　an　adaptive　fusion　strategy　is　designed　to　weight　the　importance　of　different　manifolds　in　a　data-driven　manner.　In　addition,　the　low-rank　representation　is　generalized　onto　the　fused　heterogeneous　manifolds　to　explore　the　low-dimensional　subspace　structures　embedded　in　data　for　clustering.　We　assessed　the　proposed　method　on　several　public　data　sets,　including　human　action　video,　facial　image,　and　traffic　scenario　video.　The　experimental　results　show　that　our　method　obviously　outperforms　a　number　of　state-of-the-art　clustering　methods.