Deep　learning-based　clustering　methods,　especially　those　incorporating　deep　generative　models,　have　recently　shown　noticeable　improvement　on　many　multimedia　benchmark　datasets.　However,　existing　generative　models　still　suffer　from　unstable　training,　and　the　gradient　vanishes,　which　results　in　the　inability　to　learn　desirable　embedded　features　for　clustering.　In　this　paper,　we　aim　to　tackle　this　problem　by　exploring　the　capability　of　Wasserstein　embedding　in　learning　representative　embedded　features　and　introducing　a　new　clustering　module　for　jointly　optimizing　embedding　learning　and　clustering.　To　this　end,　we　propose　Wasserstein　embedding　clustering　(WEC),　which　integrates　robust　generative　models　with　clustering.　By　directly　minimizing　the　discrepancy　between　the　prior　and　marginal　distribution,　we　transform　the　optimization　problem　of　Wasserstein　distance　from　the　original　data　space　into　embedding　space,　which　differs　from　other　generative　approaches　that　optimize　in　the　original　data　space.　Consequently,　it　naturally　allows　us　to　construct　a　joint　optimization　framework　with　the　designed　clustering　module　in　the　embedding　layer.　Due　to　the　substitutability　of　the　penalty　term　in　Wasserstein　embedding,　we　further　propose　two　types　of　deep　clustering　models　by　selecting　different　penalty　terms.　Comparative　experiments　conducted　on　nine　publicly　available　multimedia　datasets　with　several　state-of-the-art　methods　demonstrate　the　effectiveness　of　our　method.　IEEE