OPTICS in JavaScript with @kanaries/ml

Learn what OPTICS does, when to use it, and how to run OPTICS in JavaScript or TypeScript with @kanaries/ml for browser and Node.js applications.

Algorithm overview

OPTICS captures density-based cluster structure across multiple scales and handles varying-density datasets.

This algorithm is especially useful when:

DBSCAN-style sensitivity to a single epsilon is too limiting.
You need ordering information to inspect hierarchical density structure.
Noise handling is required for reliable segmentation.

@kanaries/ml makes OPTICS available in JavaScript for density-based clustering workflows where cluster shapes are irregular and noise handling matters. This is useful for web-based data exploration products that need to surface structure without assuming spherical clusters.

Running OPTICS in the JS stack means engineering teams can keep clustering logic close to interactive visualizations, upload flows, or Node.js processing jobs instead of depending on a separate Python layer.

Quick start

OPTICS in Python vs JavaScript / TypeScript

If you searched for "OPTICS in JavaScript" or "OPTICS in TypeScript", this section maps the familiar scikit-learn call to the equivalent @kanaries/ml usage for browser and Node.js runtimes.

Python

scikit-learn

from sklearn.cluster import OPTICS

X = [[0, 0], [0.1, 0.1], [5, 5], [5.2, 5.1], [10, 1]]

model = OPTICS(min_samples=2, max_eps=0.6)
labels = model.fit_predict(X)

JavaScript / TypeScript

@kanaries/ml

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

const X = [[0, 0], [0.1, 0.1], [5, 5], [5.2, 5.1], [10, 1]];

const model = new Clusters.OPTICS({ min_samples: 2, eps: 0.6 });
const labels = model.fitPredict(X);

Quick JavaScript example

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

const X = [[0, 0], [0.1, 0.1], [5, 5], [5.2, 5.1], [10, 1]];

const model = new Clusters.OPTICS({ min_samples: 2, eps: 0.6 });
const labels = model.fitPredict(X);

Detailed API reference

interface OPTICSOptions {
    min_samples?: number;
    max_eps?: number;
    metric?: Distance.IDistanceType;
    p?: number;
    eps?: number;
}
constructor(options: OPTICSOptions = {})

fitPredict(samplesX: number[][]): number[] returns cluster labels. Noise points are marked as -1.

const optics = new Clusters.OPTICS({ eps: 0.6, min_samples: 2 });
const labels = optics.fitPredict(X);

Implementation workflow

Prepare distance-scaled features and configure neighborhood constraints.
Fit and analyze reachability or extracted cluster labels.
Tune min-samples and extraction thresholds for your target behavior.

JavaScript deployment notes

OPTICS is a better fit than K-Means when your clusters have uneven density or non-convex structure.
Use it for exploratory clustering and noise detection rather than low-latency request-time scoring.
Benchmark runtime carefully on larger datasets, especially in browser environments.

OPTICS