# 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);
``````

### Clustering

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], ...]]
``````

### 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

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