(Individual) Fairness for k-Clustering

Jul 12, 2020



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 [Jung et al., 2019] 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).



About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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