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.

本文提出一种利用深度神经网络来建模聚类任务中数据间关系从而提高聚类效果的算法，名称为Deep Discriminative Clustering(DDC)。DDC采用全局和局部约束对关系进行调整，通过迭代式训练得到高水平的表征，并将其作为聚类中心进行直接聚类。实验证明，DDC在图像、文本和音频数据集上表现出比当前方法更佳的聚类效果。

深层判别聚类分析