Extreme k-Center Clustering
DOI:
https://doi.org/10.1609/aaai.v35i5.16513Keywords:
Scalability, Parallel & Distributed Systems, ClusteringAbstract
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.Downloads
Published
2021-05-18
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. https://doi.org/10.1609/aaai.v35i5.16513
Issue
Section
AAAI Technical Track on Data Mining and Knowledge Management