# kmeans

**Repository Path**: mirrors_mljs/kmeans

## Basic Information

- **Project Name**: kmeans
- **Description**: K-Means clustering
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-10-22
- **Last Updated**: 2026-04-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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.

<h3 align="center">

  <a href="https://www.zakodium.com">
    <img src="https://www.zakodium.com/brand/zakodium-logo-white.svg" width="50" alt="Zakodium logo" />
  </a>

  <p>
    Maintained by <a href="https://www.zakodium.com">Zakodium</a>
  </p>

[![NPM version](https://img.shields.io/npm/v/ml-kmeans.svg)](https://www.npmjs.com/package/ml-kmeans)
[![npm download](https://img.shields.io/npm/dm/ml-kmeans.svg)](https://www.npmjs.com/package/ml-kmeans)
[![test coverage](https://img.shields.io/codecov/c/github/mljs/spectra-processing.svg)](https://codecov.io/gh/mljs/spectra-processing)
[![license](https://img.shields.io/npm/l/ml-kmeans.svg)](https://github.com/mljs/spectra-processing/blob/main/LICENSE)

</h3>

## Installation

`npm i ml-kmeans`

## [API Documentation](https://mljs.github.io/kmeans/)

## Example

```js
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 }
]
*/
```

## Authors

- [Miguel Asencio](https://github.com/maasencioh)

## 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](http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf)

## License

[MIT](./LICENSE)