Extreme k-Center Clustering
Keywords:Scalability, Parallel & Distributed Systems, Clustering
AbstractMetric 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.
How to Cite
Bateni, M., Esfandiari, H., Fischer, M., & Mirrokni, V. (2021). Extreme k-Center Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 3941-3949. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16513
AAAI Technical Track on Data Mining and Knowledge Management