JarCastingskmeans
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.
- distance function Optional. Custom distance function. Takes two points as arguments and returns a scalar number.
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
- test Function to test new point membership
Examples
// k-means with 3 clusters. Random initialization
var res = skmeans(data,3);
// k-means with 3 clusters. Initial centroids provided
var res = skmeans(data,3,[1,5,9]);
// k-means with 3 clusters. k-means++ cluster initialization
var res = skmeans(data,3,"kmpp");
// k-means with 3 clusters. Random initialization. 10 max iterations
var res = skmeans(data,3,null,10);
// k-means with 3 clusters. Custom distance function
var res = skmeans(data,3,null,null,(x1,x2)=>Math.abs(x1-x2));
// Test new point
var res = skmeans(data,3,null,10);
res.test(6);
// Test new point with custom distance
var res = skmeans(data,3,null,10);
res.test(6,(x1,x2)=>Math.abs(x1-x2));