k-means

Note

This is one of 199 standalone projects, maintained as part of the @thi.ng/umbrella monorepo and anti-framework.

🚀 Please help me to work full-time on these projects by sponsoring me on GitHub. Thank you! ❤️

• About
• Status
• Installation
• Dependencies
• Usage examples
• API
• Authors
• License

About

Configurable k-means & k-medians (with k-means++ initialization) for n-D vectors.

Status

BETA - possibly breaking changes forthcoming

Search or submit any issues for this package

Installation

yarn add @thi.ng/k-means

ESM import:

import * as kmeans from "@thi.ng/k-means";

Browser ESM import:

<script type="module" src="https://esm.run/@thi.ng/k-means"></script>

JSDelivr documentation

For Node.js REPL:

const kmeans = await import("@thi.ng/k-means");

Package sizes (brotli'd, pre-treeshake): ESM: 884 bytes

Dependencies

• @thi.ng/api
• @thi.ng/distance
• @thi.ng/errors
• @thi.ng/random
• @thi.ng/vectors

Note: @thi.ng/api is in most cases a type-only import (not used at runtime)

Usage examples

Two projects in this repo's /examples directory are using this package:

Screenshot	Description	Live demo	Source
	Color palette generation via dominant color extraction from uploaded images	Demo	Source
	k-means clustering visualization	Demo	Source

API

Generated API docs

Example usage: Clustering cities by lat/lon location

import { HAVERSINE_LATLON } from "@thi.ng/distance";
import { kmeans, meansLatLon } from "@thi.ng/k-means";

// data sourced from:
// https://github.com/OpenDataFormats/worldcities/blob/master/src/data/cities.json
const cities = [
    { id: "anchorage", latlon: [61.21806, -149.90028] },
    { id: "berlin", latlon: [52.52437, 13.41053] },
    { id: "boston", latlon: [42.35843, -71.05977] },
    { id: "calgary", latlon: [51.05011, -114.08529] },
    { id: "cape town", latlon: [-33.92584, 18.42322] },
    { id: "detroit", latlon: [42.33143, -83.04575] },
    { id: "harare", latlon: [-17.82772, 31.05337] },
    { id: "london", latlon: [51.50853, -0.12574] },
    { id: "manila", latlon: [14.6042, 120.9822] },
    { id: "nairobi", latlon: [-1.28333, 36.81667] },
    { id: "new york", latlon: [40.71427, -74.00597] },
    { id: "paris", latlon: [48.85341, 2.3488] },
    { id: "philadelphia", latlon: [39.95233, -75.16379] },
    { id: "portland", latlon: [45.52345, -122.67621] },
    { id: "seoul", latlon: [37.566, 126.9784] },
    { id: "shanghai", latlon: [31.22222, 121.45806] },
    { id: "tokyo", latlon: [35.6895, 139.69171] },
    { id: "vancouver", latlon: [49.24966, -123.11934] },
    { id: "vienna", latlon: [48.20849, 16.37208] },
    { id: "windhoek", latlon: [-22.55941, 17.08323] },
];

// cluster based on lat/lon
const clusters = kmeans(
    5,
    cities.map((x) => x.latlon),
    {
        // custom centroid calc for geo locations
        // https://docs.thi.ng/umbrella/k-means/functions/meansLatLon.html
        strategy: meansLatLon,
        // custom distance function for geo location (default: DIST_SQ)
        dist: HAVERSINE_LATLON,
    }
);

// print each cluster
for (let c of clusters) {
    console.log(c.items.map((i) => cities[i].id));
}

// [ 'manila', 'seoul', 'shanghai', 'tokyo' ]
// [ 'berlin', 'london', 'paris', 'vienna' ]
// [ 'boston', 'detroit', 'new york', 'philadelphia' ]
// [ 'cape town', 'harare', 'nairobi', 'windhoek' ]
// [ 'anchorage', 'calgary', 'portland', 'vancouver' ]

Authors

• Karsten Schmidt

If this project contributes to an academic publication, please cite it as:

@misc{thing-k-means,
  title = "@thi.ng/k-means",
  author = "Karsten Schmidt",
  note = "https://thi.ng/k-means",
  year = 2021
}

License

© 2021 - 2024 Karsten Schmidt // Apache License 2.0