Clustering - DBScan algorithm

==============================

A node module, that uses DBScan unsupervised clustering algorithm, to return centroids and their cluster

This algorithm doesn't handle well the following:

1. Large datasets [computational complexity]
2. Number of dimensions ( > 16) - more computaitons, "curse of dimensionality"

about (2), given a fixed amount of points, the density of the points decreases exponentially. Meaning you won't be able to find cluster as you'll be wandering a lot. About "the curse", it means that Complexity: O(n^2) - space, O(n^2) - time

You'll find a pre-made 100 points 16-features vector sample file Uses stream, readline node modules

using jSHint, matchdep , stream, grunt.js

Use this with my permission only

ToC

---

1. Main app

Main app

points over map:

Install

npm install dbscan

place the distance.js where ever you want and include it, i've used an iOc style so you could adjust it and plugit in the module

Initialization

we need to initialize the distance object, you can add any distance metric you wish to distance.js

var Distance 	 = require("./lib/distance"),
	distances    = new Distance(),
	// DBScan section
	DBScan       = require('dbscan'),
	dbscan       = new DBScan(distances)

after initialization, you need to create a multi-dimensional vector, an array of arrays: [[1,2],[1,4],[2,5],[5,9],...,[10,12]]

in code we grab it via stream from a line-by-line [newline] structured flat file [so we won't have limit on memory space]

var fs = require('fs'), // File section
	readline     = require('readline'), // using the UNSTABLE readline built-in node module
	// Stream section
	stream       = require('stream'),
	points       = [],
	rl, // read-line
	in_stream;
in_stream = fs.createReadStream('./points.txt'),
rl = readline.createInterface({
							input: in_stream,
							terminal: false
						  })

rl.on('line', function(line) {
	points.push(JSON.parse(line))
});

finally we run the clustering:

	var clustering_obj = dbscan.cluster(points,distanceFunction)
    console.log('FINISHED reading ' + points.length + ' and clustering them');