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.

A parallel algorithm for approximating the silhouette using a ball

Even Faster Exact k-Means Clustering

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

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

Optimizing Yinyang K-Means Algorithm on ARMv8 Many-Core CPUs

PDF) Fast K-Means Clustering with Anderson Acceleration

PDF] Accelerating the Yinyang K-Means Algorithm Using the GPU

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

PDF) Large scale K-means clustering using GPUs

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

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