A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture

IEEE Access

Xifeng Guo¹  Gen Zhang¹  Jun Long¹  Erxue Min¹  Qiang Liu¹  Jianjing Cui¹ 

[1] College of Computer, National University of Defense Technology, Changsha, China;

关键词: Clustering;    deep learning;    data representation;    network architecture;   

DOI  :  10.1109/ACCESS.2018.2855437

来源: DOAJ

【 摘 要 】

Clustering is a fundamental problem in many data-driven application domains, and clustering performance highly depends on the quality of data representation. Hence, linear or non-linear feature transformations have been extensively used to learn a better data representation for clustering. In recent years, a lot of works focused on using deep neural networks to learn a clustering-friendly representation, resulting in a significant increase of clustering performance. In this paper, we give a systematic survey of clustering with deep learning in views of architecture. Specifically, we first introduce the preliminary knowledge for better understanding of this field. Then, a taxonomy of clustering with deep learning is proposed and some representative methods are introduced. Finally, we propose some interesting future opportunities of clustering with deep learning and give some conclusion remarks.

【 授权许可】

Unknown