  Traditional clustering methods often perform clustering with low-level
indiscriminative representations and ignore relationships between patterns,
resulting in slight achievements in the era of deep learning. To handle this
problem, we develop Deep Discriminative Clustering (DDC) that models the
clustering task by investigating relationships between patterns with a deep
neural network. Technically, a global constraint is introduced to adaptively
estimate the relationships, and a local constraint is developed to endow the
network with the capability of learning high-level discriminative
representations. By iteratively training the network and estimating the
relationships in a mini-batch manner, DDC theoretically converges and the
trained network enables to generate a group of discriminative representations
that can be treated as clustering centers for straightway clustering. Extensive
experiments strongly demonstrate that DDC outperforms current methods on eight
image, text and audio datasets concurrently.
