# @turf/clusters-dbscan > Takes a set of points and partition them into clusters according to DBSCAN's data clustering algorithm. Latest version **7.4.0** (published 2026-08-03) · MIT license · 0 weekly downloads ## Install ```sh npm install @turf/clusters-dbscan pnpm add @turf/clusters-dbscan yarn add @turf/clusters-dbscan bun add @turf/clusters-dbscan ``` ## Health **Score 80/100 (A)** — status: active. Positive: has types; esm support; no vulnerabilities; has provenance; recently updated; high maintenance score; high quality score; popular repo. Warnings: low downloads. ## Facts | | | |---|---| | Version | 7.4.0 | | Published | 2026-08-03 | | First published | 2017-07-27 | | Weekly downloads | 0 | | License | MIT | | TypeScript types | bundled | | Module format | ESM + CommonJS | | Dependencies | 7 | | Unpacked size | 39.4 KB | | Known vulnerabilities | 0 | | Install scripts | no | | Provenance | attested (GitHub Actions) | | GitHub stars | 10491 | | Author | Turf Authors | | Maintainers | rowanwins, tmcw, morganherlocker, tcql, mdfedderly, twelch, jamesmilneruk, morgan.herlocker | | Keywords | turf, geojson, cluster, clusters, clustering, density, dbscan | ## Links - npm: https://www.npmjs.com/package/@turf/clusters-dbscan - Repository: https://github.com/Turfjs/turf - Issues: https://github.com/Turfjs/turf/issues - Funding: https://opencollective.com/turf - npm.io page: https://npm.io/package/@turf/clusters-dbscan ## Dependencies (7) - [rbush](https://npm.io/package/rbush.md) ^3.0.1 - [tslib](https://npm.io/package/tslib.md) ^2.8.1 - [@turf/meta](https://npm.io/package/@turf/meta.md) 7.4.0 - [@turf/clone](https://npm.io/package/@turf/clone.md) 7.4.0 - [@turf/helpers](https://npm.io/package/@turf/helpers.md) 7.4.0 - [@turf/distance](https://npm.io/package/@turf/distance.md) 7.4.0 - [@types/geojson](https://npm.io/package/@types/geojson.md) ^7946.0.10 ## Alternatives - [@mapbox/jsonlint-lines-primitives](https://npm.io/package/@mapbox/jsonlint-lines-primitives.md) — 5.3M weekly downloads - [reftools](https://npm.io/package/reftools.md) — 3.5M weekly downloads - [@hey-api/openapi-ts](https://npm.io/package/@hey-api/openapi-ts.md) — 3.5M weekly downloads - [@mapbox/geojson-rewind](https://npm.io/package/@mapbox/geojson-rewind.md) — 2.4M weekly downloads - [turbo-stream](https://npm.io/package/turbo-stream.md) — 1.7M weekly downloads ## Recent versions - 7.4.0 (latest) — 2026-08-03 - 7.1.0-alpha.70 (prerelease) — 2024-08-09 - 7.3.5 — 2026-04-19 - 7.3.4 — 2026-02-08 - 7.3.3 — 2026-01-28 - 7.3.2 — 2026-01-14 - 7.3.1 — 2025-11-27 - 7.3.0 — 2025-11-17 - 7.2.0 — 2024-12-29 - 7.1.0 — 2024-08-09 - 7.1.0-alpha.7 — 2024-06-25 - 7.0.0 — 2024-06-07 - 7.0.0-alpha.116 — 2024-05-07 - 7.0.0-alpha.115 — 2024-04-23 - 7.0.0-alpha.114 — 2024-03-11 - … 26 more at https://npm.io/package/@turf/clusters-dbscan/versions ## README # @turf/clusters-dbscan ## Dbscan Point classification within the cluster. Type: (`"core"` | `"edge"` | `"noise"`) ## DbscanProps **Extends GeoJsonProperties** Properties assigned to each clustered point. Type: [object][1] ### Properties * `dbscan` **[Dbscan][2]?** type of point it has been classified as * `cluster` **[number][3]?** associated clusterId ## clustersDbscan Takes a set of [points][4] and partition them into clusters according to [DBSCAN's][5] data clustering algorithm. ### Parameters * `points` **[FeatureCollection][6]<[Point][4]>** to be clustered * `maxDistance` **[number][3]** Maximum Distance between any point of the cluster to generate the clusters (kilometers by default, see options) * `options` **[Object][1]** Optional parameters (optional, default `{}`) * `options.units` **Units** in which `maxDistance` is expressed, Supports all valid Turf [Units][7] (optional, default `"kilometers"`) * `options.mutate` **[boolean][8]** Allows GeoJSON input to be mutated (optional, default `false`) * `options.minPoints` **[number][3]** Minimum number of points to generate a single cluster, points which do not meet this requirement will be classified as an 'edge' or 'noise'. (optional, default `3`) ### Examples ```javascript // create random points with random z-values in their properties var points = turf.randomPoint(100, {bbox: [0, 30, 20, 50]}); var maxDistance = 100; var clustered = turf.clustersDbscan(points, maxDistance); //addToMap var addToMap = [clustered]; ``` Returns **[FeatureCollection][6]<[Point][4], [DbscanProps][9]>** Clustered Points with an additional two properties associated to each Feature:* {number} cluster - the associated clusterId * {string} dbscan - type of point it has been classified as ('core'|'edge'|'noise') [1]: https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object [2]: #dbscan [3]: https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number [4]: https://tools.ietf.org/html/rfc7946#section-3.1.2 [5]: https://en.wikipedia.org/wiki/DBSCAN [6]: https://tools.ietf.org/html/rfc7946#section-3.3 [7]: https://turfjs.org/docs/api/types/Units [8]: https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean [9]: #dbscanprops --- This module is part of the [Turfjs project](https://turfjs.org/), an open source module collection dedicated to geographic algorithms. It is maintained in the [Turfjs/turf](https://github.com/Turfjs/turf) repository, where you can create PRs and issues. ### Installation Install this single module individually: ```sh $ npm install @turf/clusters-dbscan ``` Or install the all-encompassing @turf/turf module that includes all modules as functions: ```sh $ npm install @turf/turf ``` --- _Source: https://npm.io/package/@turf/clusters-dbscan · Machine-readable twin of the npm.io package page. Health data is recomputed on every publish._