Balanced $k$-Center Clustering When $k$ Is A Constant
Computational Geometry
2017-04-11 v1
Abstract
The problem of constrained -center clustering has attracted significant attention in the past decades. In this paper, we study balanced -center cluster where the size of each cluster is constrained by the given lower and upper bounds. The problem is motivated by the applications in processing and analyzing large-scale data in high dimension. We provide a simple nearly linear time -approximation algorithm when the number of clusters is assumed to be a constant. Comparing with existing method, our algorithm improves the approximation ratio and significantly reduces the time complexity. Moreover, our result can be easily extended to any metric space.
Cite
@article{arxiv.1704.02515,
title = {Balanced $k$-Center Clustering When $k$ Is A Constant},
author = {Hu Ding},
journal= {arXiv preprint arXiv:1704.02515},
year = {2017}
}