Research map / Learning theory and algorithms / Clustering
KFC: A Scalable Approximation Algorithm for $k$−center Fair Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use KFC instead of Bera et al.'s fair-clustering LP | fair k-center clustering · 4area (α=0.45) | cost: 9.768 -> 9.421 | Helped |
| Use KFC instead of Bera et al.'s fair-clustering LP | fair k-center clustering · bank (α=0.80) | runtime (s): 117.5 -> 2.941 | Helped |
| Use KFC instead of Bera et al.'s fair-clustering LP | fair k-center clustering · census (α=0.86) | runtime (s): TLE -> 19.83 | Helped |
| Increase the number of centers k | KFC · reuters and victorian | runtime (s) | Hurt |
| Increase the number of centers k | KFC · reuters and victorian | cost | Helped |
| Tighten the fairness parameter δ | KFC · reuters | runtime (s) | No clear effect |
| Increase the number of protected groups | KFC · bank | runtime (s) | Hurt |
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