Scalable fair clustering
Name
1902.03519.pdf
Description
Accepted version
Size
1.67 MB
Format
Adobe PDF
Checksum (MD5)
9499ff43e8ffe2efe082680e6edf0600
Author(s)
Indyk, Piotr
Onak, Krzysztof
Vakilian, Ali
Wagner, Tal
Date Issued
June 2019
Journal
36th International Conference on Machine Learning, ICML 2019
Publisher
International Machine Learning Society
Citation
Backurs, Arturs et al. “Scalable fair clustering.” Paper presented at the 36th International Conference on Machine Learning, ICML 2019, Long Beach, CA, June 10, 2019 - June 15, 2019, International Machine Learning Society © 2019 The Author(s)
Version
Author's final manuscript
Abstract
We study the fair variant of the classic k-median problem introduced by Chierichetti et al. (Chierichetti et al., 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. 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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://proceedings.mlr.press/v97/backurs19a.html
