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skmeans

Super fast simple k-means and k-means++ implementation for unidimiensional and multidimensional data. Works on nodejs and browser.

Installation

npm install skmeans

Usage

NodeJS

const skmeans = require("skmeans");

var data = [1,12,13,4,25,21,22,3,14,5,11,2,23,24,15];
var res = skmeans(data,3);

Browser

<!doctype html>
<html>
<head>
	<script src="skmeans.js"></script>
</head>
<body>
	<script>
		var data = [1,12,13,4,25,21,22,3,14,5,11,2,23,24,15];
		var res = skmeans(data,3);

		console.log(res);
	</script>
</body>
</html>

Results

{
	it: 2,
	k: 3,
	idxs: [ 2, 0, 0, 2, 1, 1, 1, 2, 0, 2, 0, 2, 1, 1, 0 ],
	centroids: [ 13, 23, 3 ]
}

API

skmeans(data,k,[centroids],[iterations])

Calculates unidimiensional and multidimensional k-means 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]]
k Number of clusters
centroids Optional. Initial centroid values. If not provided, the algorith will try to choose an apropiate ones. Alternative values can be:
- "kmrand" Cluster initialization will be random, but with extra checking, so there will no be two equal initial centroids.
- "kmpp" The algorythm will use the k-means++ cluster initialization method.
iterations Optional. Maximum number of iterations. If not provided, it will be set to 10000.

The function will return an object with the following data:

it The number of iterations performed until the algorithm has converged
k The cluster size
centroids The value for each centroid of the cluster
idxs The index to the centroid corresponding to each value of the data array

README.md