基于深度学习的聚类综述  

Survey of clustering based on deep learning

作　　者：董永峰 邓亚晗[1,2,3] 董瑶 王雅琮 DONG Yongfeng;DENG Yahan;DONG Yao;WANG Yacong(School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401,China;Hebei Province Key Laboratory of Big Data Calculation(Hebei University of Technology),Tianjin 300401,China;Hebei Data Driven Industrial Intelligent Engineering Research Center(Hebei University of Technology),Tianjin 300401,China)

机构地区：[1]河北工业大学人工智能与数据科学学院,天津300401 [2]河北省大数据计算重点实验室(河北工业大学),天津300401 [3]河北省数据驱动工业智能工程研究中心(河北工业大学),天津300401

出　　处：《计算机应用》2022年第4期1021-1028,共8页journal of Computer Applications

基　　金：天津市自然科学基金资助项目(19JCZDJC40000);北航北斗技术成果转化及产业化资金资助项目(BARI2001);河北省高等学校科学技术研究项目(QN2021213)。

摘　　要：聚类是一种寻找数据之间内在结构的技术,是许多数据驱动应用领域的一个基本问题,而聚类性能在很大程度上取决于数据表示的质量。近年来,深度学习因其强大的特征提取能力被广泛地应用于聚类任务,以学习更好的特征表示,显著提高了聚类性能。首先,介绍了传统的聚类任务;然后,根据网络结构介绍了基于深度学习的聚类及代表性方法,指出了当前存在的问题,并介绍了基于深度学习的聚类在不同领域的应用;最后,对基于深度学习的聚类发展进行了总结与展望。Clustering is a technique to find the internal structure between data,which is a basic problem in many datadriven applications.Clustering performance depends largely on the quality of data representation.In recent years,deep learning is widely used in clustering tasks due to its powerful feature extraction ability,in order to learn better feature representation and improve clustering performance significantly.Firstly,the traditional clustering tasks were introduced.Then,the representative clustering methods based on deep learning were introduced according to the network structure,the existing problems were pointed out,and the applications of deep learning based clustering in different fields were presented.At last,the development of deep learning based clustering was summarized and prospected.

关 键 词：聚类 深度学习 图聚类 特征表示 网络结构 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]