Mean Shift

Algorithm overview

MeanShift detects high-density regions and infers cluster count automatically from data density.

This algorithm is especially useful when:

• You want clustering without specifying the number of clusters.
• Density peaks are more meaningful than centroid partitions.
• Your product needs adaptive grouping behavior over time.

JavaScript implementation

@kanaries/ml gives JavaScript applications access to Mean Shift for cases where clusters form around dense modes rather than around a fixed, user-specified cluster count. That can be useful in browser-based exploratory tools where users want clustering without deciding k up front.

Because the implementation is available in JS, you can experiment with bandwidth-driven clustering directly inside Node.js services or interactive frontend tools.

Interactive Mean Shift playground

Move the bandwidth control to change how local density peaks merge. The chart is recomputed with Clusters.MeanShift in your browser, and the diamonds show the modes found by the fitted model.

Quick start

from sklearn.cluster import MeanShift

X = [[0, 0], [0.1, 0.2], [3, 3], [3.2, 3.1]]

model = MeanShift(bandwidth=1.0)
labels = model.fit_predict(X)

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

const X = [[0, 0], [0.1, 0.2], [3, 3], [3.2, 3.1]];

const model = new Clusters.MeanShift(1.0);
const labels = model.fitPredict(X);

Quick JavaScript example

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

const X = [[0, 0], [0.1, 0.2], [3, 3], [3.2, 3.1]];

const model = new Clusters.MeanShift(1.0);
const labels = model.fitPredict(X);
console.log(labels);

Detailed API reference

constructor(
    bandwidth: number = 1,
    max_iter: number = 300,
    distanceType: Distance.IDistanceType = 'euclidean'
)

Methods:

• fitPredict(samplesX: number[][]): number[]
• getCentroids(): number[][]

const ms = new Clusters.MeanShift(2);
const labels = ms.fitPredict(X);
const centers = ms.getCentroids();

Implementation workflow

1. Scale numeric features and choose bandwidth heuristics.
2. Fit and inspect discovered modes and assigned labels.
3. Tune bandwidth to balance over-fragmentation and over-merging.

JavaScript deployment notes

• Spend time tuning bandwidth because it strongly controls cluster granularity.
• Mean Shift is better suited to exploratory analysis than very large real-time workloads.
• For browser apps, prefer smaller datasets or background workers because iterative shifting can be expensive.