mlsanityData to 14 Oct 2026

K*-Means: A Parameter-free Clustering Algorithm

arXiv 2505.11904 ↗First posted 17 May 2025cs.LG cs.AI cs.ITcited by 3

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Abstract (arXiv)

Clustering is a widely used and powerful machine learning technique, but its effectiveness is often limited by the need to specify the number of clusters, k, or by relying on thresholds that implicitly determine k. We introduce k*-means, a novel clustering algorithm that eliminates the need to set k or any other parameters. Instead, it uses the minimum description length principle to automatically determine the optimal number of clusters, k*, by splitting and merging clusters while also optimising the standard k-means objective. We prove that k*-means is guaranteed to converge and demonstrate experimentally that it significantly outperforms existing methods in scenarios where k is unknown. We also show that it is accurate in estimating k, and that empirically its runtime is competitive with existing methods, and scales well with dataset size.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 19 of its 43 references that are in the map. First posted on arXiv 17 May 2025.

Builds on

19 of 43 references.

References · 19

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  3. 2018Deep Clustering for Unsupervised Learning of Visual Featurescited by 1,028
  4. 2019Graph Embedded Pose Clustering for Anomaly Detectioncited by 95
  5. 2024Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approachcited by 50
  6. 2023DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learningcited by 37
  7. 2023Deep Temporal Graph Clusteringcited by 28
  8. 2023Semi-supervised learning made simple with self-supervised clusteringcited by 20
  9. 2024A Modular Approach for Multimodal Summarization of TV Showscited by 13
  10. 2023Minimum Description Length Clustering to Measure Meaningful Image Complexitycited by 10
  11. 2021Selective Pseudo-label Clusteringcited by 8
  12. 2023Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentationcited by 7
7 more
  1. 2024Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspectivecited by 7
  2. 2024Breaking the Reclustering Barrier in Centroid-based Deep Clusteringcited by 4
  3. 2024Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representationscited by 3
  4. 2024OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learningcited by 3
  5. 2025Parameter-free Video Segmentation for Vision and Language Understandingcited by 3
  6. 2024Towards a Universal Method for Meaningful Signal Detectioncited by 2
  7. 2025Local Compositional Complexity: How to Detect a Human-readable Messsagecited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Cross-Attention Calibrated Deduplication for Retrieval-Augmented Generation Systemcited by 0
  2. 2026Density-aware Hierarchical Clustering Based on Element-Categorized Connection Subgraphscited by 0
  3. 2026Automatic depth-based local center clustering via β-integrated local depth and adaptive groupingcited by 0