Efficient Parallel Algorithms for k-Center Clustering

Abstract

The k-center problem is a classic NP-hard clustering question. For contemporary massive data sets, RAM-based algorithms become impractical. Although there exist good algorithms for k-center, they are all inherently sequential. In this paper, we design and implement parallel approximation algorithms for k-center. We observe that Gonzalez's greedy algorithm can be efficiently parallelized in several MapReduce rounds, in practice, we find that two rounds are sufficient, leading to a 4-approximation. In practice, we find this parallel scheme is about 100 times faster than the sequential Gonzalez algorithm, and barely compromises solution quality. We contrast this with an existing parallel algori..