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Apriori Associator

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

Constructor Parameters

$support - minimum threshold of support, i.e. the ratio of samples which contain both X and Y for a rule "if X then Y"
$confidence - minimum threshold of confidence, i.e. the ratio of samples containing both X and Y to those containing X

use Phpml\Association\Apriori;

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

Train

To train an 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);

You can train the associator using multiple data sets, predictions will be based on all the training data.

Predict

To predict sample label use the 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

To get generated association rules, simply use the rules method.

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

Frequent item sets

To generate k-length frequent item sets, simply use the apriori method.

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

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