  Recent developed deep unsupervised methods allow us to jointly learn
representation and cluster unlabelled data. These deep clustering methods
mainly focus on the correlation among samples, e.g., selecting high precision
pairs to gradually tune the feature representation, which neglects other useful
correlations. In this paper, we propose a novel clustering framework, named
deep comprehensive correlation mining(DCCM), for exploring and taking full
advantage of various kinds of correlations behind the unlabeled data from three
aspects: 1) Instead of only using pair-wise information, pseudo-label
supervision is proposed to investigate category information and learn
discriminative features. 2) The features' robustness to image transformation of
input space is fully explored, which benefits the network learning and
significantly improves the performance. 3) The triplet mutual information among
features is presented for clustering problem to lift the recently discovered
instance-level deep mutual information to a triplet-level formation, which
further helps to learn more discriminative features. Extensive experiments on
several challenging datasets show that our method achieves good performance,
e.g., attaining $62.3\%$ clustering accuracy on CIFAR-10, which is $10.1\%$
higher than the state-of-the-art results.
