NOTE: This library has been ported to Rust. See here for a more maintained version that can also be used with NodeJS or your Browser via WASM.

fuzzy-dbscan.js

fuzzy-dbscan.js computes fuzzy clusters using the FuzzyDBSCAN algorithm [1].

Installation

Download a release or:

$ npm install fuzzy-dbscan

Usage

var FuzzyDBSCAN = require('fuzzy-dbscan');
//Browserify version only, without module loader:
//var FuzzyDBSCAN = global.FuzzyDBSCAN;

FuzzyDBSCAN() constructs a new instance of the algorithm. The functions epsMin(Number) and epsMax(Number) set the fuzzy local neighborhood radius. mPtsMin(Number) and mPtsMax(Number) set the fuzzy neighborhood density (number of points). The distance(function(a, b)) function defines the distance metric used for clustering. Once all parameters are set, you can invoke cluster([...]).

Note that when setting epsMin = epsMax and mPtsMin = mPtsMax the algorithm will reduce to classic DBSCAN. Otherwise the (soft) labels will vary between 0 and 1. Moreover, the algorithm distinguishes between CORE NOISE and BORDER points.

Example

var euclideanDistance = function(a, b) {
  return Math.sqrt(Math.pow(b.x - a.x, 2) + Math.pow(b.y - a.y, 2));
};
var fuzzyDBSCAN = FuzzyDBSCAN().epsMin(10.0).epsMax(20.0).mPtsMin(1).mPtsMax(2).distanceFn(euclideanDistance);

console.log(fuzzyDBSCAN.cluster([{x: 0, y: 0}, {x: 100, y: 100}, {x: 105, y: 105}, {x: 115, y: 115}]));

References

[1] Dino Ienco, and Gloria Bordogna. "Fuzzy extensions of the DBScan clustering algorithm." Soft Computing (2016).

Versioning

This project is maintained under the Semantic Versioning guidelines.

License

Licensed under the Apache 2.0 License. Copyright © 2018 Christoph Schulz.