• Corpus ID: 235659007

Improved Approximation Algorithms for Individually Fair Clustering

@article{Vakilian2022ImprovedAA,
  title={Improved Approximation Algorithms for Individually Fair Clustering},
  author={Ali Vakilian and Mustafa Yalçiner},
  journal={ArXiv},
  year={2022},
  volume={abs/2106.14043}
}
We consider the k -clustering problem with (cid:96) p -norm cost, which includes k -median, k means and k -center, under an individual notion of fairness proposed by Jung et al. [2020]: given a set of points P of size n , a set of k centers induces a fair clustering if every point in P has a center among its n/k closest neighbors. Mahabadi and Vakilian [2020] presented a ( p O ( p ) , 7)-bicriteria approximation for fair clustering with (cid:96) p norm cost: every point finds a center within… 

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