Extreme k-Center Clustering

MohammadHossein Bateni; Hossein Esfandiari; Manuela Fischer; Vahab Mirrokni

AAAI 2021technical16 citations

Extreme k-Center Clustering

MohammadHossein Bateni, Hossein Esfandiari, Manuela Fischer, Vahab Mirrokni

Abstract

Metric clustering is a fundamental primitive in machine learning with several applications for mining massive datasets. An important example of metric clustering is the k-center problem. While this problem has been extensively studied in distributed settings, all previous algorithms use Ω(k) space per machine and Ω(n k) total work. In this paper, we develop the first highly scalable approximation algorithm for k-center clustering, with O~(n^ε) space per machine and O~(n^(1+ε)) total work, for arbitrary small constant ε. It produces an O(log log log n)-approximate solution with k(1+o(1)) centers in O(log log n) rounds of computation.

BibTeX

@inproceedings{aaai2021_extremekcentercl,
  title = {Extreme k-Center Clustering},
  author = {MohammadHossein Bateni and Hossein Esfandiari and Manuela Fischer and Vahab Mirrokni},
  booktitle = {AAAI 2021},
  year = {2021}
}