Deep Clustering: A Comprehensive Survey

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NETL
NSTL
IEEE

外文摘要：Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering (DC), which can learn clustering-friendly representations using deep neural networks (DNNs), has been broadly applied in a wide range of clustering tasks. Existing surveys for DC mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering. To address this issue, in this article, we provide a comprehensive survey for DC in views of data sources. With different data sources, we systematically distinguish the clustering methods in terms of methodology, prior knowledge, and architecture. Concretely, DC methods are introduced according to four categories, i.e., traditional single-view DC, semi-supervised DC, deep multiview clustering (MVC), and deep transfer clustering. Finally, we discuss the open challenges and potential future opportunities in different fields of DC.

外文关键词：

Feature extractionTask analysisClustering methodsSurveysRepresentation learningTransfer learningProbability distribution

作者：

Yazhou Ren、Jingyu Pu、Zhimeng Yang、Jie Xu、Guofeng Li、Xiaorong Pu、Philip S. Yu、Lifang He

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作者单位：

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China|Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

Department of Computer Science, University of Illinois Chicago, Chicago, IL, USA

Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA

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出版年：

2025

DOI：

10.1109/TNNLS.2024.3403155

IEEE transactions on neural networks and learning systems

ISSN：

年,卷(期)：2025.36(4)

参考文献量268