A fast k-means implementation using coresets

@inproceedings{Frahling2006AFK,
  title={A fast k-means implementation using coresets},
  author={Gereon Frahling and Christian Sohler},
  booktitle={Symposium on Computational Geometry},
  year={2006}
}
In this paper we develop an efficient implementation for a k-means clustering algorithm. Our algorithm is a variant of KMHybrid [28, 20], i.e. it uses a combination of Lloyd-steps and random swaps, but as a novel feature it uses coresets to speed up the algorithm. A coreset is a small weighted set of points that approximates the original point set with respect to the considered problem. The main strength of the algorithm is that it can quickly determine clusterings of the same point set for… CONTINUE READING
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A local search approximation algorithm for k-means clustering

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