Registry / ai-ml / density-clustering

density-clustering

library1.3.0jsnpmunverified

This JavaScript package provides implementations of popular cluster analysis algorithms, including DBSCAN (Density-Based Spatial Clustering of Applications with Noise), OPTICS (Ordering Points To Identify the Clustering Structure), and K-Means. Currently at version 1.3.0, the package appears to be in a maintenance state, primarily supporting CommonJS environments as indicated by its examples and `bower` installation instructions, which suggest it hasn't fully adopted modern ESM practices or aggressive development cycles. A key differentiator is its direct, unopinionated implementation of these core algorithms for data mining and statistical analysis. While DBSCAN and OPTICS are true density-based methods, K-Means is included for broader utility, though it's important to note it does not rely on density for clustering. The library focuses on providing these fundamental tools for data scientists and developers working with clustering tasks.

ai-ml data

npm install density-clustering

github ↗package ↗

INSTALL

IMPORT

SIG · DENSITY-CLUSTERING

density-clustering

ai-mljavascriptv1.3.0

Install

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Import

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Disk

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Pass rate

0/ 6

Env Coverage0 / 6

glibc

18–22

musl

18–22

Install & Compatibility

Where this runs

tested against v? · npm install

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

node 18–226 runs

build_error

glibc

node 18–226 runs

build_error

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

DBSCAN

✓ const clustering = require('density-clustering'); const dbscan = new clustering.DBSCAN();

✗ import { DBSCAN } from 'density-clustering';

The library primarily uses CommonJS `require` syntax. Direct ESM named imports may not work without a bundler or transpiler setup for older modules.

OPTICS

✓ const clustering = require('density-clustering'); const optics = new clustering.OPTICS();

✗ import OPTICS from 'density-clustering/optics';

Classes are exposed via the main `clustering` object. Importing from subpaths or using default imports is not supported.

KMEANS

✓ const clustering = require('density-clustering'); const kmeans = new clustering.KMEANS();

✗ const KMEANS = require('density-clustering').KMEANS;

While this specific `wrong` example might technically work, the recommended pattern is to get the `clustering` object first and then access its properties for consistency.

Demonstrates how to initialize and run the DBSCAN algorithm on a sample dataset, logging the identified clusters and noise points.

const dataset = [ [1,1],[0,1],[1,0], [10,10],[10,13],[13,13], [54,54],[55,55],[89,89],[57,55] ]; const clustering = require('density-clustering'); const dbscan = new clustering.DBSCAN(); // parameters: 5 - neighborhood radius, 2 - number of points in neighborhood to form a cluster const clusters = dbscan.run(dataset, 5, 2); console.log('Clusters:', clusters); console.log('Noise points:', dbscan.noise); /* Expected Output: Clusters: [ [0,1,2], [3,4,5], [6,7,9] ] Noise points: [ 8 ] */

Debug

Known issues

gotchaWhen using the OPTICS algorithm, the initial `clusters` array returned by `run()` is often nearly identical to DBSCAN's output. To fully leverage OPTICS for varying densities and hierarchical structures, you *must* analyze the `reachability plot` generated by `optics.getReachabilityPlot()`.

fix

After running OPTICS, call `optics.getReachabilityPlot()` and implement logic to interpret the plot for multi-density or hierarchical cluster extraction as per the OPTICS algorithm specification.

affects: >=1.0.0

gotchaThe K-MEANS algorithm is included in this library but it is *not* a density-based clustering method. Its inclusion is for completeness, but users should be aware that it operates on different principles (centroid-based partitioning) compared to DBSCAN and OPTICS.

fix

Understand the fundamental differences between K-Means and density-based algorithms. Use K-Means when partitioning into a predefined number of clusters is desired, rather than identifying dense regions and noise.

affects: >=1.0.0

deprecatedThe documentation mentions `bower install density-clustering` for browser usage. Bower is largely deprecated and not recommended for modern web development. Using `npm` with a bundler (like Webpack or Rollup) is the current standard for browser integration.

fix

For browser use, install via `npm install density-clustering` and then use a JavaScript bundler (e.g., Webpack, Rollup, Parcel) to include it in your browser-side application.

affects: >=1.0.0

Errors

Common errors & fixes

ReferenceError: require is not defined

Attempting to use `require()` in an ECMAScript Module (ESM) context without proper transpilation or configuration.

fix

Ensure your Node.js project is configured for CommonJS (e.g., `"type": "commonjs"` in `package.json` or by using `.cjs` extension for files). For browser environments, use a bundler.

TypeError: clustering is not a constructor

Incorrectly trying to instantiate `new clustering()` instead of `new clustering.DBSCAN()` (or OPTICS/KMEANS).

fix

Access the specific clustering algorithm class (e.g., `DBSCAN`, `OPTICS`, `KMEANS`) as a property of the `clustering` object before instantiating it, like `new clustering.DBSCAN()`.

Error: Invalid parameters for algorithm run.

Providing an incorrect number or type of arguments to the `run` method of DBSCAN, OPTICS, or KMEANS.

fix

Refer to the documentation for the specific algorithm's `run` method. DBSCAN and OPTICS typically expect `(dataset, neighborhoodRadius, minPoints)`, while KMEANS expects `(dataset, numberOfClusters)`.

Upgrade

Version history

1.3.0latest on npm

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Dependencies

No dependency data recorded yet.

Agent activity

15 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/LukaszKrawczyk/density-clustering ↗

packagehttps://www.npmjs.com/package/density-clustering ↗