Research map / Learning theory and algorithms / Clustering
Fair Algorithms for Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply fair reassignment to the vanilla k-means solution | Vanilla k-means · bank, k-means, k=2–10, δ=0.2 | maximum additive fairness violation: 21.99 -> 1.54 | Helped |
| Apply fair reassignment to the vanilla k-means solution | Vanilla k-means · census, k-means, k=2–10, δ=0.2 | maximum additive fairness violation: 773.19 -> 1.08 | Helped |
| Apply fair reassignment to the vanilla k-means solution | Vanilla k-means · creditcard, k-means, k=2–10, δ=0.2 | maximum additive fairness violation: 192.01 -> 2.29 | Helped |
| Use Fair Algorithms for Clustering instead of Backurs et al. | k-median (k=20) · bank | clustering cost: 6.03 \times 10^5 -> 2.43 \times 10^5 | Helped |
| Use Fair Algorithms for Clustering instead of Chierichetti et al. | k-median (k=20) · bank | clustering cost: 5.55 \times 10^5 -> 2.43 \times 10^5 | Helped |
| Use Fair Algorithms for Clustering instead of Backurs et al. | k-median (k=20) · census | clustering cost: 24.1 \times 10^6 -> 4.24\times 10^6 | Helped |
| Use Fair Algorithms for Clustering instead of Chierichetti et al. | k-median (k=20) · census | clustering cost: 36.5 \times 10^6 -> 4.24\times 10^6 | Helped |
| Enforce fairness across both protected attributes rather than one (ablation) | k-means clustering · bank | balance across protected attributes | Helped |
| Enforce fairness by race rather than sex (ablation) | k-means clustering · census | balance on sex | Helped |
| Enforce fairness by sex rather than race (ablation) | k-means clustering · census | balance on race | Hurt |
| Relax the fairness threshold by increasing δ toward 1 (ablation) | Fair Algorithms for Clustering · UCI datasets, k-means | cost of fairness | Helped |
| Use iterative rounding to obtain an integral fair assignment | Fair p-Assignment · Fair (k,p)-Clustering | assignment cost: cost at most the LP value; violation at most 4Δ+3 | Helped |
Closest papers by meaning
- Fair Clustering via Alignment (ICML 2025 · similarity 0.92)
- Towards Fairer Centroids in K-means Clustering (AAAI 2024 · similarity 0.91)
- Proportional Fairness in Non-Centroid Clustering (NeurIPS 2024 · similarity 0.91)
- The Fairness-Quality Tradeoff in Clustering (NeurIPS 2024 · similarity 0.90)
- Relative Error Fair Clustering in the Weak-Strong Oracle Model (ICML 2025 · similarity 0.89)
- Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems (ICLR 2025 · similarity 0.89)
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