---
title: "Birch, Affinity Propagation, and Bisecting K-Means in JavaScript"
description: "Cluster streamed, exemplar-based, or hierarchical data in JavaScript and TypeScript with Birch, AffinityPropagation, and BisectingKMeans."
canonical_url: "https://ml.kanaries.net/docs/apis/clusters/advancedClustering"
markdown_url: "https://ml.kanaries.net/docs/apis/clusters/advancedClustering.md"
---
# Advanced clustering in JavaScript

## Algorithm overview

`Birch` incrementally compresses observations into clustering-feature subclusters, `AffinityPropagation` discovers exemplars by message passing, and `BisectingKMeans` recursively splits high-inertia groups.

## JavaScript implementation

These estimators run locally in browser or Node.js and follow the library's `fitPredict` convention. Affinity propagation stores several O(n²) matrices; as a practical browser ceiling, keep it below roughly 2,000 samples unless you have benchmarked the target device's memory budget.

## Interactive advanced clustering playground

Switch among Birch, Affinity Propagation, and Bisecting K-Means, then change each estimator's most important controls. Every view is fitted live with the corresponding `@kanaries/ml` class, including its reported centers, exemplars, or inertia.

> 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/advancedClustering).

## Quick start

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

const X = [[-2, -2], [-1.8, -2.1], [2, 2], [2.1, 1.9]];
const labels = new Clusters.Birch({ threshold: 0.5, nClusters: 3 }).fitPredict(X);
console.log(labels);
```

Birch also supports `partialFit`; AffinityPropagation exposes exemplar indices and `nIter`; BisectingKMeans exposes final centers and inertia.

## Detailed API reference

`Birch` accepts `threshold`, `branchingFactor`, and `nClusters`. `AffinityPropagation` accepts `damping`, `preference`, `maxIter`, `convergenceIter`, and `randomState`. `BisectingKMeans` accepts `nClusters`, `bisectingStrategy`, the inner K-Means iteration settings, and `randomState`.