ml-kmeans
K-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.
Installation
npm i ml-kmeans
API Documentation
Example
const kmeans = ; let data = 1 1 1 1 2 1 -1 -1 -1 -1 -1 -15;let centers = 1 2 1 -1 -1 -1; let ans = ;console;/*KMeansResult { clusters: [ 0, 0, 1, 1 ], centroids: [ { centroid: [ 1, 1.5, 1 ], error: 0.25, size: 2 }, { centroid: [ -1, -1, -1.25 ], error: 0.0625, size: 2 } ], converged: true, iterations: 1}*/
Authors
Sources
D. Arthur, S. Vassilvitskii, k-means++: The Advantages of Careful Seeding, in: Proc. of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, 2007, pp. 1027–1035. Link to article