Extreme k-Center Clustering


  • MohammadHossein Bateni Google Research
  • Hossein Esfandiari Google Research
  • Manuela Fischer ETH Zurich
  • Vahab Mirrokni Google Research


Scalability, Parallel & Distributed Systems, Clustering


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.




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