Vectorized implementation of K-means
Tomoki Otsuka, Norishige Fukushima · 2021
K-means is a widely used clustering technique that seeks to minimize the squared distance among points in the same cluster. K-means is an appealing clustering method in terms of computational speed. Also, K-means is the simplest clustering; thus, researchers select K-means as a first choice. Therefore, accelerating K-means is essential. In this study, we transform data structure from the array of structures to the structure of arrays for accelerating K-means by SIMD vectorization. Experimental results show that our implementation is faster than OpenCV’s implementation.