On k-Median clustering in high dimensions

@inproceedings{Chen2006OnKC,
  title={On k-Median clustering in high dimensions},
  author={Ke Chen},
  booktitle={SODA},
  year={2006}
}
We study approximation algorithms for <i>k</i>-median clustering. We obtain small coresets for <i>k</i>-median clustering in metric spaces as well as in Euclidean spaces. Specifically, in R<sup>d</sup>, those coresets are of size with only <i>polynomial</i> dependency on <i>d</i>. This leads to a (1 + ε)-approximation algorithm for <i>k</i>-median clustering in R<sup>d</sup>, with running time <i>O</i>(<i>ndk</i> +2<sup>(k/ε)<sup><i>o</i>(1)</sup></sup><i>d</i><sup>2</sup><i>n</i>σ), for any… CONTINUE READING
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