40 lines
1.5 KiB
Markdown
40 lines
1.5 KiB
Markdown
# K-means clustering
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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.
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This algorithm requires the number of clusters to be specified.
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### Constructor Parameters
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* $clustersNumber - number of clusters to find
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* $initialization - initialization method, default kmeans++ (see below)
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```
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$kmeans = new KMeans(2);
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$kmeans = new KMeans(4, KMeans::INIT_RANDOM);
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```
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### Clustering
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To divide the samples into clusters, simply use the `cluster` method. It returns the `array` of clusters with samples inside.
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```
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$samples = [[1, 1], [8, 7], [1, 2], [7, 8], [2, 1], [8, 9]];
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Or if you need to keep your identifiers along with yours samples you can use array keys as labels.
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$samples = [ 'Label1' => [1, 1], 'Label2' => [8, 7], 'Label3' => [1, 2]];
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$kmeans = new KMeans(2);
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$kmeans->cluster($samples);
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// return [0=>[[1, 1], ...], 1=>[[8, 7], ...]] or [0=>['Label1' => [1, 1], 'Label3' => [1, 2], ...], 1=>['Label2' => [8, 7], ...]]
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```
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### Initialization methods
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#### kmeans++ (default)
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K-means++ method selects initial cluster centers for k-mean clustering in a smart way to speed up convergence.
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It uses the DASV seeding method consists of finding good initial centroids for the clusters.
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#### random
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Random initialization method chooses completely random centroid. It gets the space boundaries to avoid placing cluster centroids too far from samples data.
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