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.
- $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]]; Or if you need to keep your indentifiers along with yours samples you can use array keys as labels. $samples = [ 'Label1' => [1, 1], 'Label2' => [8, 7], 'Label3' => [1, 2]]; $kmeans = new KMeans(2); $kmeans->cluster($samples); // return [0=>[[1, 1], ...], 1=>[[8, 7], ...]] or [0=>['Label1' => [1, 1], 'Label3' => [1, 2], ...], 1=>['Label2' => [8, 7], ...]]
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.