Research map / Learning theory and algorithms / Clustering
Individual Fairness for k-Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use local search instead of FairKCenter | Fair k-median · Diabetes | k-median cost: 1.4 | Helped |
| Use local search instead of FairKCenter | Fair k-median · Diabetes | fairness: 1.13 | Hurt |
| Use local search instead of FairKCenter | Fair k-median · Bank | k-median cost: 2.25 | Helped |
| Use local search instead of FairKCenter | Fair k-median · Bank | fairness: 1.5 | Hurt |
| Use local search instead of FairKCenter | Fair k-median · Census | k-median cost: 1.93 | Helped |
| Use local search instead of FairKCenter | Fair k-median · Census | fairness: 1.16 | Hurt |
| Use local search instead of FairKCenter | Fair k-means · Diabetes | k-means cost: 2.93 | Helped |
| Use local search instead of FairKCenter | Fair k-means · Diabetes | fairness: 1.14 | Hurt |
| Use local search instead of FairKCenter | Fair k-means · Bank | k-means cost: 2.32 | Helped |
| Use local search instead of FairKCenter | Fair k-means · Bank | fairness: 1.85 | Hurt |
| Use local search instead of FairKCenter | Fair k-means · Census | k-means cost: 1.73 | Helped |
| Use local search instead of FairKCenter | Fair k-means · Census | fairness: 1.48 | Hurt |
Closest papers by meaning
- Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems (ICLR 2025 · similarity 0.90)
- Towards Fairer Centroids in K-means Clustering (AAAI 2024 · similarity 0.90)
- Fair Clustering via Alignment (ICML 2025 · similarity 0.89)
- Parameterized Approximation Schemes for Fair-Range Clustering (NeurIPS 2024 · similarity 0.89)
- Proportional Fairness in Non-Centroid Clustering (NeurIPS 2024 · similarity 0.88)
- The Fairness-Quality Tradeoff in Clustering (NeurIPS 2024 · similarity 0.88)
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