mlsanityData to 7 Oct 2026

Clustering with Deep Learning: Taxonomy and New Methods

arXiv 1801.07648 ↗First posted 23 Jan 2018cs.LG cs.AI cs.CVcited by 45

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Abstract (arXiv)

Clustering methods based on deep neural networks have proven promising for clustering real-world data because of their high representational power. In this paper, we propose a systematic taxonomy of clustering methods that utilize deep neural networks. We base our taxonomy on a comprehensive review of recent work and validate the taxonomy in a case study. In this case study, we show that the taxonomy enables researchers and practitioners to systematically create new clustering methods by selectively recombining and replacing distinct aspects of previous methods with the goal of overcoming their individual limitations. The experimental evaluation confirms this and shows that the method created for the case study achieves state-of-the-art clustering quality and surpasses it in some cases.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2018.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 11; 2022: 11; 2023: 10; 2024: 5; 2025: 5; 2026: 2.

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  1. 2022Deep Clustering: A Comprehensive Surveycited by 78
  2. 2022A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directionscited by 73
  3. 2021Multi-Facet Clustering Variational Autoencoderscited by 28
  4. 2022A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resourcecited by 26
  5. 2022Delving into Semantic Scale Imbalancecited by 22
  6. 2021Centroid Transformers: Learning to Abstract with Attentioncited by 20
  7. 2023Label-Efficient Learning in Agriculture: A Comprehensive Reviewcited by 16
  8. 2021A Framework for Deep Constrained Clusteringcited by 14
  9. 2021Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applicationscited by 14
  10. 2021From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigationcited by 11
  11. 2024A Rapid Review of Clustering Algorithmscited by 9
  12. 2021Large-Scale Hyperspectral Image Clustering Using Contrastive Learningcited by 6
33 more
  1. 2022Review of Clustering Methods for Functional Datacited by 6
  2. 2024Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusterscited by 6
  3. 2023A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotypingcited by 5
  4. 2023CARL-G: Clustering-Accelerated Representation Learning on Graphscited by 5
  5. 2023Unsupervised Embedding Learning for Human Activity Recognition Using Wearable Sensor Datacited by 4
  6. 2019Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Datacited by 3
  7. 2021Representation Learning for Short Text Clusteringcited by 3
  8. 2022Deep Clustering With Consensus Representationscited by 3
  9. 2023HPSCAN: Human Perception-Based Scattered Data Clusteringcited by 2
  10. 2023High-dimensional Clustering onto Hamiltonian Cyclecited by 2
  11. 2026Deep Clustering with Associative Memoriescited by 2
  12. 2021Thinkback: Task-SpecificOut-of-Distribution Detectioncited by 1
  13. 2022A Deep Dive into Deep Clustercited by 1
  14. 2022Learning to Structure an Image with Few Colors and Beyondcited by 1
  15. 2022Clustering with Neural Network and Indexcited by 1
  16. 2023The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edgescited by 1
  17. 2023DMS: Differentiable Mean Shift for Dataset Agnostic Task Specific Clustering Using Side Informationcited by 1
  18. 2024Consistency Based Weakly Self-Supervised Learning for Human Activity Recognition with Wearablescited by 1
  19. 2025Mixed Data Clustering Survey and Challengescited by 1
  20. 2025Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approachescited by 1
  21. 2026CLUBench: A Clustering Benchmarkcited by 1
  22. 2021Famous Companies Use More Letters in Logo:A Large-Scale Analysis of Text Area in Logocited by 0
  23. 2021A multi-stage semi-supervised improved deep embedded clustering method for bearing fault diagnosis under the situation of insufficient labeled samplescited by 0
  24. 2021Visualizing the embedding space to explain the effect of knowledge distillationcited by 0
  25. 2022Transient motion classification through turbid volumes via parallelized single-photon detection and deep contrastive embeddingcited by 0
  26. 2022Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithmcited by 0
  27. 2023Cluster-Guided Unsupervised Domain Adaptation for Deep Speaker Embeddingcited by 0
  28. 2023Unsupervised Learning via Network-Aware Embeddingscited by 0
  29. 2024Datacube segmentation via Deep Spectral Clusteringcited by 0
  30. 2024Delivery Optimized Discovery in Behavioral User Segmentation under Budget Constraintcited by 0
  31. 2025Fast Clustering of Categorical Big Datacited by 0
  32. 2025Subjective Behaviors and Preferences in LLM: Language of Browsingcited by 0
  33. 2025A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecastingcited by 0