A Center in Your Neighborhood: Fairness in Facility Location

Jung, Cheolkon; Kannan, Sampath; Lutz, Neil

doi:10.48550/arxiv.1908.09041

Cited by 5 publications

(16 citation statements)

References 7 publications

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“…Further, we run experiments on three datasets (Diabetes, Bank, Census) that have been previously used in the context of fair clustering (e.g., see [CKLV17, CFLM19, BIO + 19, BCFN19, HJV19]). Our experiments show that in compare to the algorithm of [JKL19], the kmedian cost of our solution improves on average by a factor of 1.86, but it loses on fairness by a factor of 1.26 on average.…”

Section: Our Contributionmentioning

confidence: 88%

“…In [JKL19], this notion was first considered as a measure for fairness. Definition 2.2 (α-fair clustering [JKL19]).…”

Section: Preliminariesmentioning

confidence: 99%

“…In this paper, following the work of [JKL19], we study the clustering problem from the individual fairness point of view: the goal is to design a clustering of the input point set so that all points are treated (approximately) equally. As an example, this is important when the clustering is used in certain infrastructural decisions such as where to open a new facility to serve residents in different neighborhoods.…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned above, this notion of fair clustering was introduced in [JKL19] where the authors showed that it is possible to get a 2-approximate fair clustering, meaning that there exists a feasible solution of a set of k centers where every point p has a center within distance 2r(p). This algorithmic result is based on the previous works of [CDG06,CMM10] on metric embedding.…”

Section: Introductionmentioning

confidence: 99%

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Individual Fairness for $k$-Clustering

Mahabadi,

Vakilian

2020

Preprint

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We give a local search based algorithm for k-median (k-means) clustering from the perspective of individual fairness. More precisely, for a point x in a point set P of size n, let r(x) be the minimum radius such that the ball of radius r(x) centered at x has at least n/k points from P . Intuitively, if a set of k random points are chosen from P as centers, every point x ∈ P expects to have a center within radius r(x). An individually fair clustering provides such a guarantee for every point x ∈ P . This notion of fairness was introduced in [JKL19] where they showed how to get an approximately feasible k-clustering with respect to this fairness condition.In this work, we show how to get an approximately optimal such fair k-clustering. The k-median (k-means) cost of our solution is within a constant factor of the cost of an optimal fair k-clustering, and our solution approximately satisfies the fairness condition (also within a constant factor). Further, we complement our theoretical bounds with empirical evaluation.

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Section: Our Contributionmentioning

confidence: 88%

“…In [JKL19], this notion was first considered as a measure for fairness. Definition 2.2 (α-fair clustering [JKL19]).…”

Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Individual Fairness for $k$-Clustering

Mahabadi,

Vakilian

2020

Preprint

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“…Clustering algorithms are unsupervised machine learning algorithms that are utilized in numerous application domains, generally to group similar but unlabeled data together. Some of these application fields include biology [1], facility location [2], computer vision [3], among others. Clustering algorithms essentially find patterns from the given dataset, and then using those to generate output clusters.…”

Section: Introductionmentioning

confidence: 99%

Suspicion-Free Adversarial Attacks on Clustering Algorithms

Chhabra

Roy

Mohapatra

2020

AAAI

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Clustering algorithms are used in a large number of applications and play an important role in modern machine learning– yet, adversarial attacks on clustering algorithms seem to be broadly overlooked unlike supervised learning. In this paper, we seek to bridge this gap by proposing a black-box adversarial attack for clustering models for linearly separable clusters. Our attack works by perturbing a single sample close to the decision boundary, which leads to the misclustering of multiple unperturbed samples, named spill-over adversarial samples. We theoretically show the existence of such adversarial samples for the K-Means clustering. Our attack is especially strong as (1) we ensure the perturbed sample is not an outlier, hence not detectable, and (2) the exact metric used for clustering is not known to the attacker. We theoretically justify that the attack can indeed be successful without the knowledge of the true metric. We conclude by providing empirical results on a number of datasets, and clustering algorithms. To the best of our knowledge, this is the first work that generates spill-over adversarial samples without the knowledge of the true metric ensuring that the perturbed sample is not an outlier, and theoretically proves the above.

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A Localization Method of Ant Colony Optimization in Nonuniform Space

Qin

Zhang

2022

Sensors

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The purpose of geographic location selection is to make the best use of space. Geographic location selection contains a large amount of spatiotemporal data and constraints, resulting in too many solutions. Therefore, this paper adopts the ant colony algorithm in the meta-heuristic search method combined with the incomplete quadtree to improve the searchability of the space. This paper proposes an improved ant colony algorithm in nonuniform space to solve the P-center facility location problem. The geographic space is divided by the incomplete quadtree, and the ant colony path is constructed on the level of the quadtree division. Ant colonies can leave pheromones on multiple search paths, and optimized quadtree encoding in nonuniform space stores pheromone matrices and distance matrices. The algorithm proposed in this paper improves the pheromone diffusion algorithm and the optimization objective at the same time to update the pheromone in the nonuniform space and obtain the ideal solution. The results show that the algorithm has excellent performance in solving the location problem with good convergence accuracy and calculation time.

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A Center in Your Neighborhood: Fairness in Facility Location

Cited by 5 publications

References 7 publications

Individual Fairness for $k$-Clustering

Individual Fairness for $k$-Clustering

Suspicion-Free Adversarial Attacks on Clustering Algorithms

A Localization Method of Ant Colony Optimization in Nonuniform Space

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