density-clustering

Density Based Clustering in JavaScript

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Density Based Clustering for JavaScript
Package contains popular methods for cluster analysis in data mining:
  • DBSCAN
  • OPTICS
  • K-MEANS
Overview

DBSCAN

Density-based spatial clustering of applications with noise (DBSCAN) is one of the most popular algorithm for clustering data. http://en.wikipedia.org/wiki/DBSCAN

OPTICS

Ordering points to identify the clustering structure (OPTICS) is an algorithm for clustering data similar to DBSCAN. The main difference between OPTICS and DBSCAN is that it can handle data of varying densities. http://en.wikipedia.org/wiki/OPTICSalgorithm Important Clustering returned by OPTICS is nearly indistinguishable from a clustering created by DBSCAN. To extract different density-based clustering as well as hierarchical structure you need to analyse reachability plot generated by OPTICS. For more information visit http://en.wikipedia.org/wiki/OPTICSalgorithm#Extractingtheclusters

K-MEANS

K-means clustering is one of the most popular method of vector quantization, originally from signal processing. Although this method is not density-based, it's included in the library for completeness. http://en.wikipedia.org/wiki/K-meansclustering

Installation

Node: ```bash npm install density-clustering ``` Browser: ```bash bower install density-clustering
build
npm install gulp ```

Examples

DBSCAN

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

OPTICS

```js // REGULAR DENSITY var dataset = 1,1
,0,1,1,0, 10,10,10,11,11,10, 50,50,51,50,50,51, 100,100 ; var clustering = require('density-clustering'); var optics = new clustering.OPTICS(); // parameters: 2 - neighborhood radius, 2 - number of points in neighborhood to form a cluster var clusters = optics.run(dataset, 2, 2); var plot = optics.getReachabilityPlot(); console.log(clusters, plot); / RESULT: 0,1,2, 3,4,5, 6,7,8, 9 / ``` ```js // VARYING DENSITY var dataset = 0,0,6,0,-1,0,0,1,0,-1, 45,45,45.1,45.2,45.1,45.3,45.8,45.5,45.2,45.3, 50,50,56,50,50,52,50,55,50,51 ; var clustering = require('density-clustering'); var optics = new clustering.OPTICS(); // parameters: 6 - neighborhood radius, 2 - number of points in neighborhood to form a cluster var clusters = optics.run(dataset, 6, 2); var plot = optics.getReachabilityPlot(); console.log(clusters, plot); / RESULT: 0, 2, 3, 4, 1, 5, 6, 7, 9, 8, 10, 14, 12, 13, 11 / ```

K-MEANS

```js var dataset = 1,1
,0,1,1,0, 10,10,10,13,13,13, 54,54,55,55,89,89,57,55 ; var clustering = require('density-clustering'); var kmeans = new clustering.KMEANS(); // parameters: 3 - number of clusters var clusters = kmeans.run(dataset, 3); console.log(clusters); / RESULT: 0,1,2,3,4,5, 6,7,9, 8 / ```

Testing

Open folder and run: ```bash mocha -R spec ```

License

Software is licensed under MIT license. For more information check LICENSE file.