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

arXiv: https://arxiv.org/abs/2206.07579 · HTML: https://mlsanity.com/paper/2206.07579/

## Key facts

- Cited by 73 papers in the map (data through 14 Oct 2026).
- Builds on 112 of its 287 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 30 of 112)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Learning Representations by Maximizing Mutual Information Across Views](https://mlsanity.com/paper/1906.00910/) (2019)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1905.07953/) (2019)

## Built on by (most cited first, 30 of 73)

- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource](https://mlsanity.com/paper/2211.12875/) (2022)
- [Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget](https://mlsanity.com/paper/2304.10520/) (2023)
- [End-to-end Differentiable Clustering with Associative Memories](https://mlsanity.com/paper/2306.03209/) (2023)
- [Unsupervised Representation Learning for Time Series: A Review](https://mlsanity.com/paper/2308.01578/) (2023)
- [P2OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering](https://mlsanity.com/paper/2401.09266/) (2024)
- [Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning](https://mlsanity.com/paper/2301.00802/) (2023)
- [Homophily-enhanced Structure Learning for Graph Clustering](https://mlsanity.com/paper/2308.05309/) (2023)
- [Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking](https://mlsanity.com/paper/2401.04266/) (2024)
- [A Rapid Review of Clustering Algorithms](https://mlsanity.com/paper/2401.07389/) (2024)
- [A Survey on Deep Clustering: From the Prior Perspective](https://mlsanity.com/paper/2406.19602/) (2024)
- [Simplifying Clustering with Graph Neural Networks](https://mlsanity.com/paper/2207.08779/) (2022)
- [Find Beauty in the Rare: Contrastive Composition Feature Clustering for Nontrivial Cropping Box Regression](https://mlsanity.com/paper/2302.08662/) (2023)
- [Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters](https://mlsanity.com/paper/2402.00608/) (2024)
- [C3: Cross-instance guided Contrastive Clustering](https://mlsanity.com/paper/2211.07136/) (2022)
- [Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas](https://mlsanity.com/paper/2309.14610/) (2023)
- [An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders](https://mlsanity.com/paper/2406.02465/) (2024)
- [An Adaptive Framework for Multi-View Clustering Leveraging Conditional Entropy Optimization](https://mlsanity.com/paper/2412.17647/) (2024)
- [CCFC: Bridging Federated Clustering and Contrastive Learning](https://mlsanity.com/paper/2401.06634/) (2024)
- [Dying Clusters Is All You Need -- Deep Clustering With an Unknown Number of Clusters](https://mlsanity.com/paper/2410.09491/) (2024)
- [Breaking the Reclustering Barrier in Centroid-based Deep Clustering](https://mlsanity.com/paper/2411.02275/) (2024)
- [Deep Spectral Clustering via Joint Spectral Embedding and Kmeans](https://mlsanity.com/paper/2412.11080/) (2024)
- [TopoCL: Topological Contrastive Learning for Medical Imaging](https://mlsanity.com/paper/2603.14647/) (2026)
- [DC3DCD: unsupervised learning for multiclass 3D point cloud change detection](https://mlsanity.com/paper/2305.05421/) (2023)
- [CCFC++: Enhancing Federated Clustering through Feature Decorrelation](https://mlsanity.com/paper/2402.12852/) (2024)
- [Clustering via Self-Supervised Diffusion](https://mlsanity.com/paper/2507.04283/) (2025)
- [HPSCAN: Human Perception-Based Scattered Data Clustering](https://mlsanity.com/paper/2304.14185/) (2023)
- [Expert-Agnostic Ultrasound Image Quality Assessment using Deep Variational Clustering](https://mlsanity.com/paper/2307.02462/) (2023)
- [CDR: Conservative Doubly Robust Learning for Debiased Recommendation](https://mlsanity.com/paper/2308.08461/) (2023)
- [The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks](https://mlsanity.com/paper/2310.01144/) (2023)

## Abstract (arXiv)

Clustering is a fundamental machine learning task which has been widely studied in the literature. Classic clustering methods follow the assumption that data are represented as features in a vectorized form through various representation learning techniques. As the data become increasingly complicated and complex, the shallow (traditional) clustering methods can no longer handle the high-dimensional data type. With the huge success of deep learning, especially the deep unsupervised learning, many representation learning techniques with deep architectures have been proposed in the past decade. Recently, the concept of Deep Clustering, i.e., jointly optimizing the representation learning and clustering, has been proposed and hence attracted growing attention in the community. Motivated by the tremendous success of deep learning in clustering, one of the most fundamental machine learning tasks, and the large number of recent advances in this direction, in this paper we conduct a comprehensive survey on deep clustering by proposing a new taxonomy of different state-of-the-art approaches. We summarize the essential components of deep clustering and categorize existing methods by the ways they design interactions between deep representation learning and clustering. Moreover, this survey also provides the popular benchmark datasets, evaluation metrics and open-source implementations to clearly illustrate various experimental settings. Last but not least, we discuss the practical applications of deep clustering and suggest challenging topics deserving further investigations as future directions.