---
title: "DBScan in JavaScript with @kanaries/ml"
description: "Discover density-based clusters and noise with the DBScan JavaScript and TypeScript implementation in @kanaries/ml for browser and Node.js applications."
canonical_url: "https://ml.kanaries.net/docs/apis/clusters/dbscan"
markdown_url: "https://ml.kanaries.net/docs/apis/clusters/dbscan.md"
---
# DBScan in JavaScript

## Algorithm overview

DBSCAN groups points that are densely connected and marks sparse points as noise. It is useful when cluster count is unknown and clusters may have irregular shapes.

## JavaScript implementation

`@kanaries/ml` provides `Clusters.DBScan` for JavaScript and TypeScript workflows. It accepts an epsilon radius, a minimum sample count, and a distance metric name.

## Interactive DBSCAN playground

Tune the neighborhood radius and minimum sample count below. The cluster map is fitted live with `Clusters.DBScan`; click the chart to test whether a new point connects dense regions or remains noise.

> The HTML version includes a live clustering playground powered by @kanaries/ml. You can change the dataset and algorithm parameters, add observations, and inspect fitted clusters, noise labels, centers, or exemplars. The runnable guide and API reference continue below. [Open the HTML page](https://ml.kanaries.net/docs/apis/clusters/dbscan).

## Quick start example

```ts
import { Clusters } from '@kanaries/ml';

const X = [[0, 0], [0.1, 0], [5, 5], [5.1, 5], [20, 20]];

const dbscan = new Clusters.DBScan(0.3, 2, 'euclidean');
const labels = dbscan.fitPredict(X);
console.log(labels);
```

## Detailed API reference

```ts
new Clusters.DBScan(
  eps?: number,
  minSamples?: number,
  distanceType?: Distance.IDistanceType,
)
```

Methods:

- `fitPredict(samplesX: number[][]): number[]`

Labels are numeric cluster ids starting at `0`. Noise points are labeled `-1`.