Research map / Learning theory and algorithms / Clustering
Individual Preference Stability for Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use k-means++ instead of the other tested standard clustering algorithms | Adult dataset clustering · Adult, Euclidean, large k | number of unstable points | Helped |
| Use k-means++ instead of the other tested standard clustering algorithms | Adult dataset clustering · Adult, Euclidean, large k | maximum violation (MaxVi) | Helped |
| Use group-fair k-center instead of k-center | k-center clustering · Adult, Euclidean | IP-stability | Helped |
| Use group-fair k-center instead of k-center | k-center clustering · other datasets and metrics | IP-stability | Mixed |
| Reassign unstable points locally to their closest cluster | standard clustering algorithms · real-data experiments | IP-stability | No clear effect |
| Use the tested clustering algorithms rather than random assignment | Adult dataset clustering · Adult, Euclidean, k=100 | number of unstable points | Helped |
Closest papers by meaning
- Constant Approximation for Individual Preference Stable Clustering (NeurIPS 2023 · similarity 0.96)
- Proportional Fairness in Clustering: A Social Choice Perspective (NeurIPS 2024 · similarity 0.87)
- Proportional Fairness in Non-Centroid Clustering (NeurIPS 2024 · similarity 0.87)
- Competitively Consistent Clustering (ICML 2025 · similarity 0.87)
- The Fairness-Quality Tradeoff in Clustering (NeurIPS 2024 · similarity 0.86)
- Fair Clustering via Alignment (ICML 2025 · similarity 0.85)
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