php-ml/docs/machine-learning/association/apriori.md

Apriori Associator

Association rule learning based on Apriori algorithm for frequent item set mining.

Constructor Parameters

• $support - confidence, minimum relative amount of frequent item set in train sample
• $confidence - confidence, minimum relative amount of item set in frequent item sets

use Phpml\Association\Apriori;

$associator = new Apriori($support = 0.5, $confidence = 0.5);

Train

To train a associator simply provide train samples and labels (as array). Example:

$samples = [['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta'], ['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta']];
$labels  = [];

use Phpml\Association\Apriori;

$associator = new Apriori($support = 0.5, $confidence = 0.5);
$associator->train($samples, $labels);

Predict

To predict sample label use predict method. You can provide one sample or array of samples:

$associator->predict(['alpha','theta']);
// return [[['beta']]]

$associator->predict([['alpha','epsilon'],['beta','theta']]);
// return [[['beta']], [['alpha']]]

Associating

Get generated association rules simply use rules method.

$associator->getRules();
// return [['antecedent' => ['alpha', 'theta'], 'consequent' => ['beta], 'support' => 1.0, 'confidence' => 1.0], ... ]

Frequent item sets

Generating k-length frequent item sets simply use apriori method.

$associator->apriori();
// return [ 1 => [['alpha'], ['beta'], ['theta'], ['epsilon']], 2 => [...], ...]