mlsanityData to 7 Oct 2026

Efficient Parameter-free Clustering Using First Neighbor Relations

arXiv 1902.11266 ↗First posted 28 Feb 2019cs.CVcited by 96

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Abstract (arXiv)

We present a new clustering method in the form of a single clustering equation that is able to directly discover groupings in the data. The main proposition is that the first neighbor of each sample is all one needs to discover large chains and finding the groups in the data. In contrast to most existing clustering algorithms our method does not require any hyper-parameters, distance thresholds and/or the need to specify the number of clusters. The proposed algorithm belongs to the family of hierarchical agglomerative methods. The technique has a very low computational overhead, is easily scalable and applicable to large practical problems. Evaluation on well known datasets from different domains ranging between 1077 and 8.1 million samples shows substantial performance gains when compared to the existing clustering techniques.

Key facts. Cited by 96 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Feb 2019.

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Citing papers per year: 2021: 15; 2022: 17; 2023: 15; 2024: 20; 2025: 21; 2026: 8.

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  1. 2021Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptationcited by 176
  2. 2021AutoNovel: Automatically Discovering and Learning Novel Visual Categoriescited by 116
  3. 2023Dynamic Conceptional Contrastive Learning for Generalized Category Discoverycited by 90
  4. 2022PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discoverycited by 85
  5. 2021Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulationcited by 82
  6. 2021Towards Discovery and Attribution of Open-world GAN Generated Imagescited by 66
  7. 2021Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentationcited by 58
  8. 2022Towards Open-Set Object Detection and Discoverycited by 44
  9. 2022Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformerscited by 39
  10. 2022MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Seriescited by 28
  11. 2024Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domainscited by 27
  12. 2023CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discoverycited by 26
38 more
  1. 2023Federated Generalized Category Discoverycited by 26
  2. 2021Face, Body, Voice: Video Person-Clustering with Multiple Modalitiescited by 24
  3. 2022The Anatomy of Video Editing: A Dataset and Benchmark Suite for AI-Assisted Video Editingcited by 24
  4. 2024PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Imagescited by 24
  5. 2022C2F-TCN: A Framework for Semi and Fully Supervised Temporal Action Segmentationcited by 23
  6. 2023Entropic Open-set Active Learningcited by 22
  7. 2021Iterative Contrast-Classify For Semi-supervised Temporal Action Segmentationcited by 19
  8. 2021Learning Hierarchical Graph Neural Networks for Image Clusteringcited by 18
  9. 2022SLIC: Self-Supervised Learning with Iterative Clustering for Human Action Videoscited by 17
  10. 2022Learning Hierarchical Image Segmentation For Recognition and By Recognitioncited by 16
  11. 2024ConceptExpress: Harnessing Diffusion Models for Single-image Unsupervised Concept Extractioncited by 16
  12. 2021Weakly Supervised Person Search with Region Siamese Networkscited by 14
  13. 2022Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reductioncited by 14
  14. 2023Unsupervised Representation Learning for Time Series: A Reviewcited by 14
  15. 2021The Pursuit of Knowledge: Discovering and Localizing Novel Categories using Dual Memorycited by 13
  16. 2023STEPs: Self-Supervised Key Step Extraction and Localization from Unlabeled Procedural Videoscited by 13
  17. 2024Beyond Training: Dynamic Token Merging for Zero-Shot Video Understandingcited by 13
  18. 2021Towards Unsupervised Domain Adaptation for Deep Face Recognition under Privacy Constraints via Federated Learningcited by 10
  19. 2023Leveraging triplet loss for unsupervised action segmentationcited by 10
  20. 2024ProMerge: Prompt and Merge for Unsupervised Instance Segmentationcited by 10
  21. 2022Optimizing Performance of Federated Person Re-identification: Benchmarking and Analysiscited by 9
  22. 2022Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentationcited by 9
  23. 2023Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clusteringcited by 9
  24. 2023A Prototype-Based Neural Network for Image Anomaly Detection and Localizationcited by 7
  25. 2022Federated Unsupervised Domain Adaptation for Face Recognitioncited by 6
  26. 2025Skeleton Motion Words for Unsupervised Skeleton-Based Temporal Action Segmentationcited by 6
  27. 2024FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneitycited by 5
  28. 2025Category Discovery: An Open-World Perspectivecited by 5
  29. 2023Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person Searchcited by 4
  30. 2024Rehearsal-free Federated Domain-incremental Learningcited by 4
  31. 2024Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Datacited by 4
  32. 2022Self-supervised Video-centralised Transformer for Video Face Clusteringcited by 3
  33. 2022CGUA: Context-Guided and Unpaired-Assisted Weakly Supervised Person Searchcited by 3
  34. 2022Unsupervised Audio-Visual Lecture Segmentationcited by 3
  35. 2023Differentiable Random Partition Modelscited by 3
  36. 2024AuToMATo: An Out-Of-The-Box Persistence-Based Clustering Algorithmcited by 3
  37. 2024Muscles in Time: Learning to Understand Human Motion by Simulating Muscle Activationscited by 3
  38. 20242by2: Weakly-Supervised Learning for Global Action Segmentationcited by 3

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