PDF] Yinyang K-Means: A Drop-In Replacement of the Classic K-Means

Yinyang K-means is a drop-in replacement of the classic K-Means with an order of magnitude higher performance, and significantly outperforms prior K- means algorithms consistently across all experimented data sets, cluster numbers, and machine configurations. 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 prior 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--makes Yinyang K-means a drop-in replacement of the classic K-means with an order of magnitude higher performance.

PDF] A Hybrid MPI/OpenMP Parallelization of $K$ -Means Algorithms

An efficient K-means clustering algorithm for tall data

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PDF] On the Efficiency of K-Means Clustering: Evaluation

K-means-G*: Accelerating k-means clustering algorithm utilizing

PDF) Improving Scalable K-Means++

CPI-model-based analysis of sparse k-means clustering algorithms

R Functions: K-Means Partitioning

Using a Set of Triangle Inequalities to Accelerate K-means

CPI-model-based analysis of sparse k-means clustering algorithms

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CPI-model-based analysis of sparse k-means clustering algorithms

PDF] K-means clustering using random matrix sparsification