Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup

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Yufei Ding, Yue Zhao, Xipeng Shen, Madanlal Musuvathi, Todd Mytkowicz ;
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:579-587, 2015.

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.

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