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  • title: Improved Approximation Algorithms for Individually Fair Clustering
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            Improved Approximation Algorithms for Individually Fair Clustering
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            Improved Approximation Algorithms for Individually Fair Clustering

            Mar 28, 2022

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            Ali Vakilian

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            Mustafa Yalçıner

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            About

            We consider the k-clustering problem with ℓ_p-norm cost, which includes k-median, k-means and k-center cost functions, 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 for every point v∈ P, v can find a center among its n/k closest neighbors. Recently, Mahabadi and Vakilian [2020] showed how to get a ( p^O(p),7)-bicriteria approximation for the problem of fair k-clustering with ℓ_p-norm cost…

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