Yinyang K-Means: A Drop-in Replacement of the Classic K-Means with Consistent Speedup
Abstract
This paper presents Yinyang K-means, a new algorithm for K-means clustering. By clustering the centers in the initial stage, and leveraging efficiently maintained lower and upper bounds between a point and centers, it more effectively avoids unnecessary distance calculations than prior algorithms. It significantly outperforms classic K-means and prior alternative K-means algorithms consistently across all experimented data sets, cluster numbers, and machine configurations. The consistent, superior performance—plus its simplicity, user-control of overheads, and guarantee in producing the same clustering results as the standard K-means does—makes Yinyang K-means a drop-in replacement of the classic K-means with an order of magnitude higher performance.
Cite
Text
Ding et al. "Yinyang K-Means: A Drop-in Replacement of the Classic K-Means with Consistent Speedup." International Conference on Machine Learning, 2015.Markdown
[Ding et al. "Yinyang K-Means: A Drop-in Replacement of the Classic K-Means with Consistent Speedup." International Conference on Machine Learning, 2015.](https://mlanthology.org/icml/2015/ding2015icml-yinyang/)BibTeX
@inproceedings{ding2015icml-yinyang,
title = {{Yinyang K-Means: A Drop-in Replacement of the Classic K-Means with Consistent Speedup}},
author = {Ding, Yufei and Zhao, Yue and Shen, Xipeng and Musuvathi, Madanlal and Mytkowicz, Todd},
booktitle = {International Conference on Machine Learning},
year = {2015},
pages = {579-587},
volume = {37},
url = {https://mlanthology.org/icml/2015/ding2015icml-yinyang/}
}