A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions Article Swipe
Sheng Zhou
,
Hongjia Xu
,
Zhuonan Zheng
,
Jiawei Chen
,
Zhao Li
,
Jiajun Bu
,
Jia Wu
,
Xin Wang
,
Wenwu Zhu
,
Martin Ester
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3689036
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3689036
Clustering is a fundamental machine learning task, which aim 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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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1145/3689036
- https://dl.acm.org/doi/pdf/10.1145/3689036
- OA Status
- bronze
- Cited By
- 49
- References
- 204
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403465936
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Raw OpenAlex JSON
- OpenAlex ID
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- DOI
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https://doi.org/10.1145/3689036Digital Object Identifier
- Title
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A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future DirectionsWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-16Full publication date if available
- Authors
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Sheng Zhou, Hongjia Xu, Zhuonan Zheng, Jiawei Chen, Zhao Li, Jiajun Bu, Jia Wu, Xin Wang, Wenwu Zhu, Martin EsterList of authors in order
- Landing page
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https://doi.org/10.1145/3689036Publisher landing page
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https://dl.acm.org/doi/pdf/10.1145/3689036Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3689036Direct OA link when available
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Computer science, Taxonomy (biology), Cluster analysis, Data science, Artificial intelligence, Information retrieval, Data mining, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
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49Total citation count in OpenAlex
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2025: 42, 2024: 7Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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