---
title: "NearestNeighbors Search in JavaScript and TypeScript"
description: "Run unsupervised nearest-neighbor and radius queries with brute-force, KD Tree, or Ball Tree search in browser and Node.js using @kanaries/ml."
canonical_url: "https://ml.kanaries.net/docs/apis/neighbors/nearestNeighbors"
markdown_url: "https://ml.kanaries.net/docs/apis/neighbors/nearestNeighbors.md"
---
# NearestNeighbors Search in JavaScript

## Algorithm overview

Nearest-neighbor search finds the closest reference samples without requiring labels. It supports similarity lookup, retrieval, deduplication, local analysis, and the search layer behind many non-parametric models.

## JavaScript implementation

`Neighbors.NearestNeighbors` provides the familiar sklearn-style `fit`, `kneighbors`, and `radiusNeighbors` workflow in browser and Node.js. Choose brute force explicitly or reuse the library's KD Tree and Ball Tree search structures. Only the training matrix is serialized; indexes are rebuilt safely for each query.

## Quick start

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

const search = new Neighbors.NearestNeighbors({
  nNeighbors: 2,
  algorithm: 'kdTree',
});
search.fit([[0], [2], [5]]);

console.log(search.kneighbors([[1]]));
// { distances: [[1, 1]], indices: [[0, 1]] }
```

## Detailed API reference

Constructor options are `nNeighbors`, `radius`, `algorithm` (`auto`, `brute`, `kdTree`, or `ballTree`), `leafSize`, `metric`, and Minkowski `p`. `kneighbors(X?, nNeighbors?, returnDistance?)` returns sorted distances and indices; omitting `X` queries the fitted samples while excluding each sample itself. `radiusNeighbors` follows the same pattern for a distance threshold.