# Deep Clustering: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2210.04142 · HTML: https://mlsanity.com/paper/2210.04142/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- Builds on 132 of its 276 references that are in the map.
- First posted on arXiv 9 Oct 2022.

## Builds on (most cited first, 30 of 132)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)

## Built on by (most cited first, 30 of 78)

- [A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances](https://mlsanity.com/paper/2505.17799/) (2025)
- [A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource](https://mlsanity.com/paper/2211.12875/) (2022)
- [Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios](https://mlsanity.com/paper/2303.17245/) (2023)
- [End-to-end Differentiable Clustering with Associative Memories](https://mlsanity.com/paper/2306.03209/) (2023)
- [Deep Multi-View Subspace Clustering with Anchor Graph](https://mlsanity.com/paper/2305.06939/) (2023)
- [Quantum Vision Clustering](https://mlsanity.com/paper/2309.09907/) (2023)
- [Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning](https://mlsanity.com/paper/2301.00802/) (2023)
- [A Comprehensive Survey on Spectral Clustering with Graph Structure Learning](https://mlsanity.com/paper/2501.13597/) (2025)
- [A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery](https://mlsanity.com/paper/2304.07699/) (2023)
- [A Survey on Deep Clustering: From the Prior Perspective](https://mlsanity.com/paper/2406.19602/) (2024)
- [Variational Graph Generator for Multi-View Graph Clustering](https://mlsanity.com/paper/2210.07011/) (2022)
- [Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters](https://mlsanity.com/paper/2402.00608/) (2024)
- [Organizing Unstructured Image Collections using Natural Language](https://mlsanity.com/paper/2410.05217/) (2024)
- [C3: Cross-instance guided Contrastive Clustering](https://mlsanity.com/paper/2211.07136/) (2022)
- [Riemannian Optimization on Relaxed Indicator Matrix Manifold](https://mlsanity.com/paper/2503.20505/) (2025)
- [TabClustPFN: A Prior-data Fitted Network for Tabular Data Clustering](https://mlsanity.com/paper/2601.21656/) (2026)
- [CCFC: Bridging Federated Clustering and Contrastive Learning](https://mlsanity.com/paper/2401.06634/) (2024)
- [A Novel Deep Learning Tractography Fiber Clustering Framework for Functionally Consistent White Matter Parcellation Using Multimodal Diffusion MRI and Functional MRI](https://mlsanity.com/paper/2411.01859/) (2024)
- [Breaking the Reclustering Barrier in Centroid-based Deep Clustering](https://mlsanity.com/paper/2411.02275/) (2024)
- [Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance](https://mlsanity.com/paper/2503.11017/) (2025)
- [DC3DCD: unsupervised learning for multiclass 3D point cloud change detection](https://mlsanity.com/paper/2305.05421/) (2023)
- [Self-Tuning Spectral Clustering for Speaker Diarization](https://mlsanity.com/paper/2410.00023/) (2024)
- [Clustering via Self-Supervised Diffusion](https://mlsanity.com/paper/2507.04283/) (2025)
- [Neural Capacitated Clustering](https://mlsanity.com/paper/2302.05134/) (2023)
- [HPSCAN: Human Perception-Based Scattered Data Clustering](https://mlsanity.com/paper/2304.14185/) (2023)
- [Triple-CFN: Separating Concepts and Features Enhances Machine Abstract Reasoning Ability](https://mlsanity.com/paper/2403.03190/) (2024)
- [Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2406.08524/) (2024)
- [Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning](https://mlsanity.com/paper/2411.07700/) (2024)
- [Data relativistic uncertainty framework for low-illumination anime scenery image enhancement](https://mlsanity.com/paper/2512.21944/) (2025)
- [Deep Clustering with Associative Memories](https://mlsanity.com/paper/2601.00963/) (2026)

## 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.