Clustering　has　been　a　hot　research　topic　in　unsupervised　learning.　Recently,　the　Generative　Adversarial　Networks　(GANs)　has　achieved　good　results　in　clustering　tasks　such　as　ClusterGAN.　However,　this　type　of　model　could　not　work　well　on　class　imbalanced　data.　In　this　paper,　the　spatial　attention　and　class　balanced　term　are　adopted　to　improve　the　data　clustering.　The　proposed　Spatial　Attention　GAN　(SAGAN)　can　effectively　rebalance　the　feature　maps　and　achieve　more　reliable　clustering　when　the　number　of　samples　in　the　dataset　for　each　class　is　not　balanced.　Experiments　show　the　promising　results　and　the　potential　of　the　method　for　unsupervised　clustering.