Unsupervised　clustering,　which　is　extensively　employed　in　deep　learning　and　computer　vision　as　a　fundamental　technique,　has　attracted　much　attention　in　recent　years.　Deep　embedding　clustering　often　uses　auto-encoders　to　learn　representations　for　clustering.　However,　auto-encoders　tend　to　corrupt　the　learning　representations　when　simultaneously　learning　embedded　representations　and　performing　clustering.　In　this　paper,　we　propose　a　Deep　Clustering　via　Variational　Auto-Encoder　(DC-VAE)　of　mutual　information　maximization.　First,　we　formulate　the　deep　clustering　problem　as　learning　soft　cluster　assignments　within　the　framework　of　variational　auto-encoder.　Second,　we　impose　mutual　information　maximization　on　the　observed　data　and　the　representations　to　prevent　soft　cluster　assignments　from　distorting　learning　representations.　Third,　we　derive　a　new　generalization　evidence　lower　bound　objects　related　to　several　previous　models　and　introduce　parameters　to　balance　learning　informative　representations　and　clustering.　It　is　shown　that　the　proposed　model　can　significantly　boost　the　performance　of　clustering　by　learning　effective　and　reliable　representations　for　downstream　machine　learning　tasks.　Through　experimental　results　on　several　datasets,　we　demonstrate　that　the　proposed　model　is　competitive　with　existing　state-of-the-arts　on　multiple　performance　metrics.　©　2020　Elsevier　B.V.