Journal Article10.48550/arXiv.2207.11337
Fair Range k-center
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TL;DR: This work proposes a variant of fairness which restricts each group’s number of centers with both a lower bound (minority-protection) and an upper bound (restricted-domination) and provides both an offline and one-pass streaming algorithm for the problem.
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Abstract: We study the problem of fairness in k-centers clustering on data with disjoint demographic groups. Specifically, this work proposes a variant of fairness which restricts each group's number of centers with both a lower bound (minority-protection) and an upper bound (restricted-domination), and provides both an offline and one-pass streaming algorithm for the problem. In the special case where the lower bound and the upper bound is the same, our offline algorithm preserves the same time complexity and approximation factor with the previous state-of-the-art. Furthermore, our one-pass streaming algorithm improves on approximation factor, running time and space complexity in this special case compared to previous works. Specifically, the approximation factor of our algorithm is 13 compared to the previous 17-approximation algorithm, and the previous algorithms' time complexities have dependence on the metric space's aspect ratio, which can be arbitrarily large, whereas our algorithm's running time does not depend on the aspect ratio.
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Citations
Doubly Constrained Fair Clustering
TL;DR: In this paper , the authors consider the two most prominent demographic representation fairness notions in clustering: (1) Group Fairness (GF) where the different demographic groups are supposed to have close to population-level representation in each cluster and (2) Diversity in Center Selection (DS), where the selected centers should have close-to population level representation of each group.
Approximation Algorithms for Fair Range Clustering
TL;DR: In this article , the authors studied the fair range clustering problem in which the data points are from different demographic groups and the goal is to pick $k$ centers with the minimum clustering cost such that each group is at least minimally represented in the centers set and no group dominates the center set.
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Uniform Guidelines on Employee Selection Procedures
Teva J. Scheer
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TL;DR: The Uniform Guidelines on Employee Selection Procedures (UGS) as mentioned in this paper were issued in 1978 by five agencies of the federal government to assist employers in complying with the employment non-discrimination requirements of the Civil Rights Act of 1964.
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The Capacitated K -Center Problem
Samir Khuller,Yoram J. Sussmann +1 more
TL;DR: The capacitated K-center problem is a basic facility location problem, where the authors are asked to locate K facilities in a graph and to assign vertices to facilities, so as to minimize the maximum distance from a vertex to the facility to which it is assigned.
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Fair Algorithms for Clustering
Suman K. Bera,Deeparnab Chakrabarty,Nicolas J. Flores,Maryam Negahbani +3 more
- 08 Jan 2019
TL;DR: This work significantly generalizes the seminal work of Chierichetti this http URL and transforms any vanilla clustering solution into a fair one incurring only a slight loss in quality.
•Proceedings Article
Fair k-Centers via Maximum Matching
Matthew D. Jones,Thy Nguyen,Huy Nguyen +2 more
- 12 Jul 2020
TL;DR: This paper combines the best of each algorithm by presenting a linear-time algorithm with a guaranteed 3-approximation factor and provides empirical evidence of both the algorithm’s runtime and effectiveness.