K-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.
npm i ml-kmeans
const { kmeans } = require('ml-kmeans');
let data = [
[1, 1, 1],
[1, 2, 1],
[-1, -1, -1],
[-1, -1, -1.5],
];
let centers = [
[1, 2, 1],
[-1, -1, -1],
];
let ans = kmeans(data, 2, { initialization: centers });
console.log(ans);
/*
KMeansResult {
clusters: [ 0, 0, 1, 1 ],
centroids: [ [ 1, 1.5, 1 ], [ -1, -1, -1.25 ] ],
converged: true,
iterations: 2,
distance: [Function: squaredEuclidean]
}
*/
console.log(ans.computeInformation(data));
/*
[
{ centroid: [ 1, 1.5, 1 ], error: 0.5, size: 2 },
{ centroid: [ -1, -1, -1.25 ], error: 0.125, size: 2 }
]
*/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