TY - JOUR
AU - Bateni, MohammadHossein
AU - Esfandiari, Hossein
AU - Fischer, Manuela
AU - Mirrokni, Vahab
PY - 2021/05/18
Y2 - 2024/05/27
TI - Extreme k-Center Clustering
JF - Proceedings of the AAAI Conference on Artificial Intelligence
JA - AAAI
VL - 35
IS - 5
SE - AAAI Technical Track on Data Mining and Knowledge Management
DO - 10.1609/aaai.v35i5.16513
UR - https://ojs.aaai.org/index.php/AAAI/article/view/16513
SP - 3941-3949
AB - 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.
ER -