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Poster

Scalable Fair Clustering

Arturs Backurs · Piotr Indyk · Krzysztof Onak · Baruch Schieber · Ali Vakilian · Tal Wagner

Pacific Ballroom #84

Keywords: [ Combinatorial Optimization ] [ Fairness ] [ Large Scale Learning and Big Data ]


Abstract: We study the fair variant of the classic k-median problem introduced by (Chierichetti et al., NeurIPS 2017) in which the points are colored, and the goal is to minimize the same average distance objective as in the standard $k$-median problem while ensuring that all clusters have an ``approximately equal'' number of points of each color. (Chierichetti et al., NeurIPS 2017) proposed a two-phase algorithm for fair $k$-clustering. In the first step, the pointset is partitioned into subsets called fairlets that satisfy the fairness requirement and approximately preserve the k-median objective. In the second step, fairlets are merged into k clusters by one of the existing k-median algorithms. The running time of this algorithm is dominated by the first step, which takes super-quadratic time. In this paper, we present a practical approximate fairlet decomposition algorithm that runs in nearly linear time.

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