Contrastive　learning　is　widely　used　in　deep　image　clustering　due　to　its　ability　to　learn　discriminative　representations.　However,　some　studies　simply　combined　contrastive　learning　with　clustering.　This　line　of　works　often　ignores　semantic　meaningful　representations　and　leads　to　suboptimal　performance.　In　this　paper,　we　propose　a　new　deep　image　clustering　framework　called　Nearest　Neighbor　Contrastive　Clustering　(NNCC),　which　fuses　contrastive　learning　with　neighbor　relation　mining.　During　training,　contrastive　learning　and　neighbor　relation　mining　are　updated　alternately,　where　the　former　is　conducted　in　the　backward　pass,　while　the　latter　is　employed　in　the　forward　pass.　Specially,　we　empirically　find　that　data　augmentation　is　an　effective　technique　for　generating　nearest　neighbors　manually.　A　stronger　data　augmentation　means　more　nearest　neighbors　involved　for　learning　powerful　discriminative　representations　in　the　contrastive　learning.　Due　to　effective　neighbor　relation　mining,　the　proposed　framework　learns　more　semantic　meaningful　representations　with　contrastive　learning　and　obtains　more　accurate　image　clusters.　Through　experimental　results　on　six　image　datasets,　the　proposed　framework　defeats　compared　state-of-the-arts　clustering　methods.　©　2021　Elsevier　B.V.