Extreme k-Center Clustering

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