Clustering　is　a　crucial　but　challenging　task　in　pattern　analysis　and　machine　learning.　Recent　many　deep　clustering　methods　combining　representation　learning　with　cluster　techniques　emerged.　These　deep　clustering　methods　mainly　focus　on　the　correlation　among　samples　and　ignore　the　relationship　between　samples　and　their　representations.　In　this　paper,　we　propose　a　novel　end-to-end　clustering　framework,　namely　variational　deep　embedding　clustering　by　augmented　mutual　information　maximization　(VCAMI).　From　the　perspective　of　VAE,　we　prove　that　minimizing　reconstruction　loss　is　equivalent　to　maximizing　the　mutual　information　of　the　input　and　its　latent　representation.　This　provides　a　theoretical　guarantee　for　us　to　directly　maximize　the　mutual　information　instead　of　minimizing　reconstruction　loss.　Therefore　we　proposed　the　augmented　mutual　information　which　highlights　the　uniqueness　of　the　representations　while　discovering　invariant　information　among　similar　samples.　Extensive　experiments　on　several　challenging　image　datasets　show　that　the　VCAMI　achieves　good　performance.　we　achieve　state-of-the-art　ACC　results　for　clustering　on　MNIST　(99.5%)　and　CIFAR-10　(65.4　%)　to　the　best　of　our　knowledge.