By A Mystery Man Writer
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.
Algorithms, Free Full-Text
Large scale K-means clustering using GPUs
Entropy, Free Full-Text
PDF] K-means clustering using random matrix sparsification
Using a Set of Triangle Inequalities to Accelerate K-means
PDF] On the Efficiency of K-Means Clustering: Evaluation
K-Means Clustering Part 2
Accelerating Spherical k-Means
PDF] Yinyang K-Means: A Drop-In Replacement of the Classic K-Means
PDF) Even Faster Exact k-Means Clustering
CPI-model-based analysis of sparse k-means clustering algorithms
PDF) A Hybrid MPI/OpenMP Parallelization of K-Means Algorithms
Reuse-centric k-means configuration - ScienceDirect