Recognition　of　wafer　map　defect　patterns　is　essential　for　evaluating　the　reliability　of　micro-electronic　manufacturing.　Due　to　the　difficulty　of　labeling,　the　available　large-scale　wafer　maps　are　raw　data　without　labeling.　The　scarcity　of　labeled　samples　reduces　the　defect　pattern　recognition　accuracy　of　popular　deep　convolutional　neural　networks.　To　overcome　this　problem,　we　propose　a　wafer　map　deep　clustering　(WMDC)　model.　It　learns　generic　representations　from　unlabeled　datasets　in　an　unsupervised　manner.　A　prototype　metric　loss　during　training　helps　to　learn　the　semantic　features　of　the　categories.　We　improve　the　recognition　accuracy　of　the　model　when　trained　using　scarce　labeled　data　by　transferring　the　weights　of　unsupervised　pretraining.　Experiments　on　WM811K　and　MixedWM38　wafer　datasets　demonstrate　that　the　WMDC　model　is　capable　of　obtaining　robust　prior　representations　from　the　unlabeled　wafer　maps.　Accuracies　of　97.43%　and　98.74%　are　obtained　when　fine-tuning　using　scarce　labeled　data　from　both　datasets,　respectively.