Monte Carlo approximation certificates for k-means clustering

arXiv: Machine Learning, 2017.

Cited by: 3|Bibtex|Views5|

Abstract:

Efficient algorithms for $k$-means clustering frequently converge to suboptimal partitions, and given a partition, it is difficult to detect $k$-means optimality. In this paper, we develop an a posteriori certifier of approximate optimality for $k$-means clustering. The certifier is a sub-linear Monte Carlo algorithm based on Peng and Wei...More

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