mlsanityData to 7 Oct 2026

Deep Clustering: A Comprehensive Survey

arXiv 2210.04142 ↗First posted 9 Oct 2022cs.LGcited by 78

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Abstract (arXiv)

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, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks. Existing surveys for deep clustering mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering. To address this issue, in this paper we provide a comprehensive survey for deep clustering in views of data sources. With different data sources and initial conditions, we systematically distinguish the clustering methods in terms of methodology, prior knowledge, and architecture. Concretely, deep clustering methods are introduced according to four categories, i.e., traditional single-view deep clustering, semi-supervised deep clustering, deep multi-view clustering, and deep transfer clustering. Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). Builds on 132 of its 276 references that are in the map. First posted on arXiv 9 Oct 2022.

Builds on

132 of 276 references.

References · 132

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50 most cited of 132 All 132 (JSON)

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Citing papers per year: 2022: 4; 2023: 12; 2024: 19; 2025: 28; 2026: 15.

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  1. 2025A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advancescited by 28
  2. 2022A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resourcecited by 26
  3. 2023Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarioscited by 19
  4. 2023End-to-end Differentiable Clustering with Associative Memoriescited by 15
  5. 2023Deep Multi-View Subspace Clustering with Anchor Graphcited by 13
  6. 2023Quantum Vision Clusteringcited by 13
  7. 2023Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learningcited by 12
  8. 2025A Comprehensive Survey on Spectral Clustering with Graph Structure Learningcited by 10
  9. 2023A Clustering Framework for Unsupervised and Semi-supervised New Intent Discoverycited by 9
  10. 2024A Survey on Deep Clustering: From the Prior Perspectivecited by 9
  11. 2022Variational Graph Generator for Multi-View Graph Clusteringcited by 7
  12. 2024Organizing Unstructured Image Collections using Natural Languagecited by 7
38 more
  1. 2022C3: Cross-instance guided Contrastive Clusteringcited by 6
  2. 2024Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusterscited by 6
  3. 2025Riemannian Optimization on Relaxed Indicator Matrix Manifoldcited by 5
  4. 2024Breaking the Reclustering Barrier in Centroid-based Deep Clusteringcited by 4
  5. 2025Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidancecited by 4
  6. 2026TabClustPFN: A Prior-Fitted Network for Tabular Data Clusteringcited by 4
  7. 2023DC3DCD: unsupervised learning for multiclass 3D point cloud change detectioncited by 3
  8. 2024CCFC: Bridging Federated Clustering and Contrastive Learningcited by 3
  9. 2024Self-Tuning Spectral Clustering for Speaker Diarizationcited by 3
  10. 2024A Novel Deep Learning Tractography Fiber Clustering Framework for Functionally Consistent White Matter Parcellation Using Multimodal Diffusion MRI and Functional MRIcited by 3
  11. 2025Clustering via Self-Supervised Diffusioncited by 3
  12. 2023Neural Capacitated Clusteringcited by 2
  13. 2023HPSCAN: Human Perception-Based Scattered Data Clusteringcited by 2
  14. 2024Triple-CFN: Separating Concepts and Features Enhances Machine Abstract Reasoning Abilitycited by 2
  15. 2024Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networkscited by 2
  16. 2024Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learningcited by 2
  17. 2026Deep Clustering with Associative Memoriescited by 2
  18. 2022Clustering with Neural Network and Indexcited by 1
  19. 2024Funny-Valen-Tine: Planning Solution Distribution Enhances Machine Abstract Reasoning Abilitycited by 1
  20. 2024Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classificationcited by 1
  21. 2024SiMilarity-Enhanced Homophily for Multi-View Heterophilous Graph Clusteringcited by 1
  22. 2025Consistent Amortized Clustering via Generative Flow Networkscited by 1
  23. 2025Enhancing Martian Terrain Recognition with Deep Constrained Clusteringcited by 1
  24. 2025Towards Explainable Deep Clustering for Time Series Datacited by 1
  25. 2025Community detection robustness of graph neural networkscited by 1
  26. 2025DMVFC: Deep Learning Based Functionally Consistent Tractography Fiber Clustering Using Multimodal Diffusion MRI and Functional MRIcited by 1
  27. 2025An Adaptive Resonance Theory-based Topological Clustering Algorithm with a Self-Adjusting Vigilance Parametercited by 1
  28. 2025Data relativistic uncertainty framework for low-illumination anime scenery image enhancementcited by 1
  29. 2026How to Achieve the Intended Aim of Deep Clustering Now, without Deep Learningcited by 1
  30. 2026CLUBench: A Clustering Benchmarkcited by 1
  31. 2023Transferable Deep Clustering Modelcited by 0
  32. 2023Efficient and Effective Deep Multi-view Subspace Clusteringcited by 0
  33. 2023Patch-Based Deep Unsupervised Image Segmentation using Graph Cutscited by 0
  34. 2024From Discrete to Continuous: Deep Fair Clustering With Transferable Representationscited by 0
  35. 2024Latent Embedding Clustering for Occlusion Robust Head Pose Estimationcited by 0
  36. 2024SPINEX-Clustering: Similarity-based Predictions with Explainable Neighbors Exploration for Clustering Problemscited by 0
  37. 2024Deep Clustering via Distribution Learningcited by 0
  38. 2024Deep Clustering of Remote Sensing Scenes through Heterogeneous Transfer Learningcited by 0

50 most cited of 78 All 78 (JSON)