Research map / Learning theory and algorithms / Clustering
Constant Approximation for Individual Preference Stable Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ball carving with clusters assigned to greedy k-center centers | Average-distance IP clustering · General metric | IP-stability approximation factor: O(n) -> O(1) | Helped |
| Use the greedy k-center clustering | Max-IP stability · General metric | approximation factor: 3-approximate | Helped |
| Use Kruskal-style minimum-spanning-tree edge selection, stopping at k components | Min-IP clustering · General metric | Min-IP stability: optimally | Helped |
| Use Min-IP-Clustering instead of k-means++ | Min-IP clustering · Adult | maximum and mean violation: up to a factor of 5x | Helped |
| Use IP-Clustering instead of k-means++ | Average-distance IP clustering · Adult | IP stability | Hurt |
| Use IP-Clustering instead of k-means++ | Average-distance IP clustering · Synthetic k-means++ hard instance | maximum and mean violation | Helped |
| Assign leftover points to the first eligible center rather than the closest center | Ball-Carving · General metric | IP-stability approximation factor: O(1) | Helped |
| Randomly k-color graph vertices | Graph clustering · Unweighted, undirected graphs with k=O(√n/log n) | IP-stability approximation factor: O(1) | Helped |
Closest papers by meaning
- Individual Preference Stability for Clustering (ICML 2022 · similarity 0.96)
- Competitively Consistent Clustering (ICML 2025 · similarity 0.87)
- Proportional Fairness in Non-Centroid Clustering (NeurIPS 2024 · similarity 0.85)
- Proportional Fairness in Clustering: A Social Choice Perspective (NeurIPS 2024 · similarity 0.85)
- Fair Clustering via Alignment (ICML 2025 · similarity 0.84)
- Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems (ICLR 2025 · similarity 0.84)
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