# @alkocats/k-means > A typescript implementation of the k-means algorithm with different customization capabilities. Latest version **1.0.5** (published 2019-11-08) · MIT license · 0 weekly downloads ## Install ```sh npm install @alkocats/k-means pnpm add @alkocats/k-means yarn add @alkocats/k-means bun add @alkocats/k-means ``` ## Health **Score 25/100 (F)** — status: abandoned. Positive: has types; no vulnerabilities; high quality score. Warnings: low downloads; no esm support. Negative: abandoned; low maintenance score. ## Facts | | | |---|---| | Version | 1.0.5 | | Published | 2019-11-08 | | First published | 2019-10-23 | | Weekly downloads | 0 | | License | MIT | | TypeScript types | bundled | | Module format | CommonJS | | Dependencies | 0 | | Unpacked size | 43.7 KB | | Known vulnerabilities | 0 | | Install scripts | no | | GitHub stars | 0 | | Author | Benjamin Ricchiuto | | Maintainers | alkocats | | Keywords | alkocats, k-means, ai, cluster | ## Links - npm: https://www.npmjs.com/package/@alkocats/k-means - Repository: https://github.com/alkocats/k-means - Homepage: https://github.com/alkocats/k-means#readme - Issues: https://github.com/alkocats/k-means/issues - npm.io page: https://npm.io/package/@alkocats/k-means ## Alternatives - [@expo/fingerprint](https://npm.io/package/@expo/fingerprint.md) — 6.2M weekly downloads - [@azure/monitor-opentelemetry-exporter](https://npm.io/package/@azure/monitor-opentelemetry-exporter.md) — 850.0K weekly downloads - [@azure/monitor-opentelemetry](https://npm.io/package/@azure/monitor-opentelemetry.md) — 624.0K weekly downloads - [@posthog/ai](https://npm.io/package/@posthog/ai.md) — 423.3K weekly downloads - [fakefilter](https://npm.io/package/fakefilter.md) — 63.9K weekly downloads ## Recent versions - 1.0.5 (latest) — 2019-11-08 - 1.0.4 — 2019-11-08 - 1.0.3 — 2019-11-05 - 1.0.2 — 2019-11-05 - 1.0.1 — 2019-11-05 - 1.0.0 — 2019-10-29 - 0.2.4 — 2019-10-29 - 0.2.3 — 2019-10-29 - 0.2.2 — 2019-10-29 - 0.2.1 — 2019-10-28 - 0.2.0 — 2019-10-28 - 0.1.7 — 2019-10-28 - 0.1.6 — 2019-10-28 - 0.1.5 — 2019-10-25 - 0.1.4 — 2019-10-25 - … 3 more at https://npm.io/package/@alkocats/k-means/versions ## README # k-means [![Build Status](https://travis-ci.org/alkocats/k-means.svg?branch=master)](https://travis-ci.org/alkocats/k-means) [![Test Coverage](https://api.codeclimate.com/v1/badges/35aa6a4af9216b9fd46f/test_coverage)](https://codeclimate.com/github/alkocats/k-means/test_coverage) [![Maintainability](https://api.codeclimate.com/v1/badges/35aa6a4af9216b9fd46f/maintainability)](https://codeclimate.com/github/alkocats/k-means/maintainability) [![npm version](https://badge.fury.io/js/%40alkocats%2Fk-means.svg)](https://badge.fury.io/js/%40alkocats%2Fk-means) [![MIT License](https://img.shields.io/github/license/alkocats/k-means.svg)](https://github.com/alkocats/k-means/blob/master/LICENSE) [![Known Vulnerabilities](https://snyk.io/test/github/alkocats/k-means/badge.svg)](https://snyk.io/test/github/alkocats/k-means) This package is a typescript implementation of the k-means algorithm with different customization capabilities. - [Installation](#installation) - [Usage](#usage) - [Options](#options) - [Metrics](#metrics) - [Centroid Calculators](#centroid-calculators) - [CentroidSelection](#centroidselection) - [EmptyAction](#emptyaction) - [Contributing](#contributing) - [License](#license) ## Installation Use npm to install k-means: ```bash npm install k-means ``` ## Usage Simplest setup for the usage of k-means: ```typescript import { KMeans, Vector } from '@alkocats/k-means' const kMeans = new KMeans({ clusterCount: 2 }); const points: Vector[] = [ [1, 1], [1.5, 2], [3, 4], [5, 7], [3.5, 5], [4.5, 5], [3.5, 4.5], ]; const result = kMeans.fit(points); // As the starting centroids are set randomly, this values might change // or one cluster might even get deleted console.log(result.meanSquaredError); // 0.9252 console.log(result.iterations); // 6 console.log(result.clusterIndices); // [ 1, 1, 0, 0, 0, 0, 0 ] console.log(result.clusters[0].centroid); // [ 3.9, 5.1 ] console.log(result.clusters[0].vectors); // [ [ 3, 4 ], [ 5, 7 ], [ 3.5, 5 ], [ 4.5, 5 ], [ 3.5, 4.5 ] ] console.log(result.clusters[1].centroid); // [ 1.25, 1.5 ] console.log(result.clusters[1].vectors); // [ [ 1, 1 ], [ 1.5, 2 ] ] ``` ### Options | Name | Type | Default | | -------------------- | -------------------- | ------------------------------ | | `metric` | `Metric` | `new EuclidianDistance()` | | `centroidCalculator` | `CentroidCalculator` | `new MeanCentroidCalculator()` | | `clusterCount` | `number` | `1` | | `maxIterations` | `number` | `100` | | `centroidSelection` | `CentroidSelection` | `CentroidSelection.RANDOM` | | `centroids` | `Vector[] \| Matrix` | `null` | | `emptyAction` | `EmptyAction` | `EmptyAction.DROP` | ### Metrics - `new EuclidianDistance()`: Calculates the euclidian distance between two vectors with the same number of elements (n). - `new ManhattanDistance()`: Calculates the manhattan distance between two vectors with the same number of elements (n). ### Centroid Calculators - `new MeanCentroidCalculator()`: Calculates the center of an array of vectors according to the mean of the vectors. - `new MedianCentroidCalculator()`: Calculates the center of an array of vectors according to the median of the vectors. ### CentroidSelection - `CentroidSelection.RANDOM`: Generates n (accodring to `clusterCount`) starting centroids by selecting random points within the vector limits. - `CentroidSelection.PREDEFINED`: Takes the centroids given by `centroids` as the starting centroids. - `CentroidSelection.KMEANSPLUSPLUS`: Uses the k-means++ algorithm to determine the starting centroid. ### EmptyAction - `EmptyAction.DROP`: Deletes a cluster if it does not have any vectors assigned to. - `EmptyAction.ERROR`: Throws an error if any cluster does not have any vectors assigned to. ## Contributing Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate. ## License [MIT](https://choosealicense.com/licenses/mit/) --- _Source: https://npm.io/package/@alkocats/k-means · Machine-readable twin of the npm.io package page. Health data is recomputed on every publish._