Research map / Learning theory and algorithms / Clustering
Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift Algorithm
Read the paperCheck 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 feature-weighted distance and learn feature weights during blurring mean shift | Blurring Mean Shift · GLIOMA | NMI: 0.546 -> 0.706 | Helped |
| use feature-weighted distance and learn feature weights during blurring mean shift | Blurring Mean Shift · Appendicitis | ARI: 0.223 -> 0.434 | Helped |
| use feature-weighted distance and learn feature weights during blurring mean shift | Blurring Mean Shift · Zoo | NMI: 0.841 -> 0.925 | Helped |
| use feature-weighted distance and learn feature weights during blurring mean shift | Blurring Mean Shift · Mammographic | ARI: 0.001 -> 0.351 | Helped |
| use feature-weighted distance and learn feature weights during blurring mean shift | Blurring Mean Shift · Yale | ARI: 0.027 -> 0.485 | Helped |
| use feature-weighted distance and learn feature weights during blurring mean shift | Blurring Mean Shift · nci9 | NMI: 0.394 -> 0.686 | Helped |
| use feature-weighted distance and learn feature weights during blurring mean shift | Blurring Mean Shift · Lymphoma | ARI: 0.458 -> 0.604 | Helped |
| use feature-weighted distance and learn feature weights during blurring mean shift | Blurring Mean Shift · Movement Libras | ARI: 0.185 -> 0.532 | Helped |
| use feature-weighted distance and learn feature weights during blurring mean shift | Blurring Mean Shift · GCM | ARI: 0.536 -> 0.714 | Helped |
| vary bandwidth h across 0.1, 0.5, 0.8, and 1 (ablation) | WBMS · real-life datasets | ARI: GLIOMA: 0.587, 0.618, 0.618, 0.524; Appendicitis: 0.218, 0.324, 0.401, 0.434 | Mixed |
| vary bandwidth h across 0.1, 0.5, 0.8, and 1 (ablation) | WBMS · real-life datasets | ARI: Zoo: 0.953, 0.912, 0.754, 0.754; Yale: 0.485, 0.414, 0.234, 0.001 | Mixed |
| vary lambda across 1, 5, 10, and 20 (ablation) | WBMS · real-life datasets | ARI: Appendicitis: 0.257, 0.352, 0.434, 0.398; Zoo: 0.717, 0.717, 0.906, 0.953 | Mixed |
Closest papers by meaning
- On Convergence of Epanechnikov Mean Shift (AAAI 2018 · similarity 0.89)
- UEQMS: UMAP Embedded Quick Mean Shift Algorithm for High Dimensional Clustering (AAAI 2023 · similarity 0.88)
- Quickshift++: Provably Good Initializations for Sample-Based Mean Shift (ICML 2018 · similarity 0.87)
- Weighted Conformal Clustering (NeurIPS 2026 · similarity 0.83)
- MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations (ICML 2024 · similarity 0.83)
- Capturing the denoising effect of PCA via compression ratio (NeurIPS 2024 · similarity 0.82)
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.