K-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.
Maintained by Zakodium
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:
[ { centroid: [ 1, 1.5, 1 ], error: 0.25, size: 2 },
{ centroid: [ -1, -1, -1.25 ], error: 0.0625, size: 2 } ],
converged: true,
iterations: 1
}
*/
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
FAQs
K-Means clustering
The npm package ml-kmeans receives a total of 46,406 weekly downloads. As such, ml-kmeans popularity was classified as popular.
We found that ml-kmeans demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 7 open source maintainers collaborating on the project.
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