1.7 KiB
1.7 KiB
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 => [...], ...]