A deep clustering network is desired for data streams because of its aptitude in extracting natural features thus bypassing the laborious feature engineering step. While automatic construction of the deep networks in streaming environments remains an open issue, it is also hindered by the expensive labeling cost of data streams rendering the increasing demand for unsupervised approaches. This paper presents an unsupervised approach of deep clustering network construction on the fly via simultaneous deep learning and clustering termed Autonomous Deep Clustering Network (ADCN). It combines the feature extraction layer and autonomous fully connected layer in which both network width and depth are self-evolved from data streams based on the bias-variance decomposition of reconstruction loss. The self-clustering mechanism is performed in the deep embedding space of every fully connected layer while the final output is inferred via the summation of cluster prediction score. Further, a latent-based regularization is incorporated to resolve the catastrophic forgetting issue. A rigorous numerical study has shown that ADCN produces better performance compared to its counterparts while offering fully autonomous construction of ADCN structure in streaming environments with the absence of any labeled samples for model updates. To support the reproducible research initiative, codes, supplementary material, and raw results of ADCN are made available in \url{https://tinyurl.com/AutonomousDCN}.

提出了一种无监督的深度自动聚类网络（ADCN）的构建方法，它结合了自动提取特征的层和自治的全连接层，通过重建损失进行自我进化和自我聚类，没有标记样本，能够在流式环境中完全自主地构造ADCN结构。

流式环境下的无监督连续学习