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    density-clustering
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    1.3.0 • Public • Published

    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/OPTICS_algorithm

    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/OPTICS_algorithm#Extracting_the_clusters

    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-means_clustering

    Installation

    Node:

    npm install density-clustering

    Browser:

    bower install density-clustering
    # build 
    npm install
    gulp

    Examples

    DBSCAN

    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

    // 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]
    ]
    */
    // 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

    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:

    mocha -R spec

    License

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

    Install

    npm i density-clustering

    DownloadsWeekly Downloads

    126,175

    Version

    1.3.0

    License

    MIT

    Last publish

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