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    sdbscan

    0.3.3 • Public • Published

    sdbscan

    Super fast density based spatial clustering DBSCAN implementation for unidimiensional and multidimensional data. Works on nodejs and browser.

    Installation

    npm install sdbscan
    

    Usage

    NodeJS

    const sdbscan = require("sdbscan");
     
    var data = [0, 1, 100, 101, 2, 102, 3, 104, 4, 103, 105, 5];
    var res = sdbscan(data,2,3);

    Browser

    <!doctype html>
    <html>
    <head>
        <script src="sdbscan.js"></script> 
    </head>
    <body>
        <script>
            var data = [0,1,100,101,2,102,3,104,4,103,105,5];
            var res = sdbscan(data,2,3);
     
            console.log(data);
            console.log(res);
        </script> 
    </body>
    </html>

    Results

    {
        "noise": [],
        "clusters": [
        {
          "id": 0,
          "data": [5,4,3,2,1,0]
        },
        {
          "id": 1,
          "data": [105,103,104,102,101,100]
        }
      ]
    }

    API

    sdbscan(data,epsilon,min)

    Calculates unidimiensional and multidimensional dbscan clustering on data. Parameters are:

    • data Unidimiensional or multidimensional array of values to be clustered. for unidimiensional data, takes the form of a simple array [1,2,3.....,n]. For multidimensional data, takes a NxM array [[1,2],[2,3]....[n,m]]
    • epsilon Maximum distance for two points to be considered in the same region.
    • min Minimal region size. If a region for a point is lesser than min, this point will be considered as noise (cannot be included in any group).

    The function will return an object with the following data:

    • noise Points that cannot be added to any cluster.
    • clusters An array of clusters, with an ID and the data points belonging to it.

    Install

    npm i sdbscan

    DownloadsWeekly Downloads

    19

    Version

    0.3.3

    License

    MIT

    Last publish

    Collaborators

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