A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-10-16 DOI:10.1145/3689036
Sheng Zhou, Hongjia Xu, Zhuonan Zheng, Jiawei Chen, Zhao Li, Jiajun Bu, Jia Wu, Xin Wang, Wenwu Zhu, Martin Ester
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Abstract

Clustering is a fundamental machine learning task which aims at assigning instances into groups so that similar samples belong to the same cluster while dissimilar samples belong to different clusters. Shallow clustering methods usually assume that data are collected and expressed as feature vectors within which clustering is performed. However, clustering high-dimensional data, such as images, texts, videos, and graphs, poses significant challenges for clustering tasks, such as indiscriminate representation and intricate relationships among instances. Over the past decades, deep learning has achieved remarkable success in effective representation learning and modeling complex relationships. Motivated by these advancements, Deep Clustering seeks to improve clustering outcomes through deep learning techniques, garnering considerable interest from both academia and industry. Despite many contributions to this vibrant area of research, the lack of systematic analysis and a comprehensive taxonomy has hindered progress in this field. In this survey, we first explore how deep learning can be integrated into deep clustering and identify two fundamental components: the representation learning module and the clustering module. Then we summarize and analyze the representative design of these two modules. Furthermore, we introduce a novel taxonomy of deep clustering based on how these two modules interact, specifically through multistage, generative, iterative, and simultaneous approaches. In addition, we present well-known benchmark datasets, evaluation metrics, and open-source tools to clearly demonstrate different experimental approaches. Finally, we examine the practical applications of deep clustering and propose challenging areas for future research.
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深度聚类综合调查:分类、挑战和未来方向
聚类是一项基本的机器学习任务,其目的是将实例分配到不同的组中,使相似的样本属于同一个组,而不相似的样本属于不同的组。浅层聚类方法通常假定数据已收集并表示为特征向量,并在特征向量内进行聚类。然而,图像、文本、视频和图形等高维数据的聚类对聚类任务提出了巨大挑战,例如无差别表示和实例之间错综复杂的关系。过去几十年来,深度学习在有效表示学习和复杂关系建模方面取得了显著成就。在这些进步的推动下,深度聚类试图通过深度学习技术改善聚类结果,这引起了学术界和工业界的极大兴趣。尽管对这一充满活力的研究领域做出了很多贡献,但缺乏系统分析和全面的分类方法阻碍了这一领域的进展。在本调查中,我们首先探讨了如何将深度学习集成到深度聚类中,并确定了两个基本组成部分:表征学习模块和聚类模块。然后,我们总结并分析了这两个模块的代表性设计。此外,我们还根据这两个模块的交互方式,特别是通过多级、生成、迭代和同步方法,介绍了一种新颖的深度聚类分类法。此外,我们还介绍了著名的基准数据集、评估指标和开源工具,以清楚地展示不同的实验方法。最后,我们探讨了深度聚类的实际应用,并提出了未来研究的挑战领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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