Swift implementation of the k-means++ algorithm that can operate on a collection of SIMD vectors of any length.
k-Means is an algorithm for partitioning a collection of points into clusters based on the cluster with the nearest mean value to each point. k-Means++ is an improved algorithm for choosing the initial cluster centers to avoid suboptimal clustering.
This library contains extensions to Collection that perform k-Means++ clustering on SIMD values of any length, which can represent points in Euclidean space, colors in formats like RGB or HSV, or just about anything else.