In　many　real　applications　of　machine　learning　and　data　mining,　we　are　often　confronted　with　high-dimensional　data　represented　by　heterogeneous　features　or　views,　which　describe　different　perspectives　of　the　data.　Efficiently　clustering　such　data　is　a　challenge.　To　address　this　problem,　we　propose　a　unified　and　embedded　framework　referred　to　as　multi-view　embedded　clustering　with　trace　ratio　(MECTR),　which　performs　dimensionality　reduction　and　clustering　simultaneously,　and　adaptively　controls　the　interactions　among　different　views　at　the　same　time.　Within　this　framework,　we　are　able　not　only　to　obtain　multiple　discriminative　subspaces　synchronously,　but　also　keep　the　clustering　results　consistent　among　different　views.　We　also　develop　an　alternate　iterative　optimization　strategy　to　learn　the　common　clustering　indicator,　multiple　discriminative　subspaces　and　weights　for　heterogeneous　features　with　convergence.　Comprehensive　experiments　on　synthesis　dataset　and　three　benchmark　datasets　demonstrate　the　superiority　of　the　proposed　work.　(c)　2018　Elsevier　B.V.　All　rights　reserved.