Research map / Learning theory and algorithms / Clustering
Fair Clustering Through Fairlets
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use fairlet decomposition before clustering | Fair k-center and k-median · (1, k)-fair center | approximation ratio: 3 | Helped |
| Use flow-based fairlet decomposition before clustering | Fair k-center · (1/t', k)-fair center | approximation ratio: 4 | Helped |
| Use perfectly balanced fairlets before k-median | Fair k-median · (1, k)-fair median | approximation ratio: 2+\sqrt{3}+\epsilon | Helped |
| Use flow-based fairlet decomposition before k-median | Fair k-median · (1/t', k)-fair median | approximation ratio: t'+1+\sqrt{3}+\epsilon | Helped |
| Enforce minimum cluster balance using fair clustering rather than classical clustering | Clustering algorithms · Diabetes, Bank, and Census | balance | Helped |
| Enforce minimum cluster balance using fair clustering rather than classical clustering | Clustering algorithms · Diabetes, Bank, and Census | clustering cost | Hurt |
| Increase the number of clusters without enforcing fairness | Classical k-center and k-median · Diabetes, Bank, and Census | balance | Hurt |
Closest papers by meaning
- Fair Algorithms for Clustering (NeurIPS 2019 · similarity 0.94)
- 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)
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