K-means clustering

The K-Means algorithm clusters data by trying to separate samples in n groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares. This algorithm requires the number of clusters to be specified.

Constructor Parameters

  • $clustersNumber - number of clusters to find
  • $initialization - initialization method, default kmeans++ (see below)
$kmeans = new KMeans(2);
$kmeans = new KMeans(4, KMeans::INIT_RANDOM);


To divide the samples into clusters simply use cluster method. It's return the array of clusters with samples inside.

$samples = [[1, 1], [8, 7], [1, 2], [7, 8], [2, 1], [8, 9]];

$kmeans = new KMeans(2);
// return [0=>[[1, 1], ...], 1=>[[8, 7], ...]] 

Initialization methods

kmeans++ (default)

K-means++ method selects initial cluster centers for k-mean clustering in a smart way to speed up convergence. It use the DASV seeding method consists of finding good initial centroids for the clusters.


Random initialization method chooses completely random centroid. It get the space boundaries to avoid placing clusters centroid too far from samples data.