Research map / Learning theory and algorithms / Clustering
Efficient Parameter-Free Clustering Using First Neighbor Relations
Check your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use FINCH instead of Kmeans | Clustering · REUTERS | NMI: 41.75 -> 44.88 | Helped |
| Use FINCH instead of Kmeans | Clustering · STL-10 | NMI: 85.59 -> 85.05 | Hurt |
| Use FINCH instead of SC | Clustering · MNIST_10k | NMI: 97.43 -> 97.55 | Helped |
| Use FINCH instead of SC | Clustering · MNIST_70k | NMI: 98.77 -> 98.84 | Helped |
| Use FINCH instead of Kmeans | Large-scale face clustering · BBTs01 | NMI: 71.82 -> 89.79 | Helped |
| Refine FINCH hierarchy to the true cluster count | FINCH on BBTs01 · BBTs01 (true #C=5) | NMI: 89.79 -> 91.57 | Helped |
| Use FINCH instead of Kmeans | Large-scale face clustering · BFs05 | NMI: 71.85 -> 82.46 | Helped |
| Train a feature embedding with FINCH pseudo-labels rather than cluster the base features | FINCH deep clustering · STL-10 | ACC (%): 85.28 -> 95.24 | Helped |
| Train a feature embedding with FINCH pseudo-labels rather than cluster the base features | FINCH deep clustering · REUTERS-10K | ACC (%): 66.14 -> 81.46 | Helped |
| Train a feature embedding with FINCH pseudo-labels rather than cluster the base features | FINCH deep clustering · MNIST_70k_PIXELS | ACC (%): 74.00 -> 91.89 | Helped |
| Change the Affinity Propagation preference to -100 | Affinity Propagation · Mice Protein | NMI: 59.10 -> 37.93 | Hurt |
| Change the Affinity Propagation preference to -100 | Affinity Propagation · MNIST_10k | NMI: 69.97 -> 67.13 | No clear effect |
Closest papers by meaning
- A Sampling-Based Approach for Efficient Clustering in Large Datasets (CVPR 2022 · similarity 0.92)
- An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity (AAAI 2022 · similarity 0.90)
- Ultrametric Cluster Hierarchies: I Want ‘em All! (NeurIPS 2025 · similarity 0.90)
- Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search (NeurIPS 2017 · similarity 0.89)
- Simple, Scalable and Effective Clustering via One-Dimensional Projections (NeurIPS 2023 · similarity 0.89)
- New Bounds on the Cohesion of Complete-link and Other Linkage Methods for Agglomerative Clustering (ICML 2024 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.