Package Exports

sdbscan

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Readme

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.