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61 lines
1.8 KiB
Markdown
61 lines
1.8 KiB
Markdown
# Apriori Associator
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Association rule learning based on [Apriori algorithm](https://en.wikipedia.org/wiki/Apriori_algorithm) for frequent item set mining.
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### Constructor Parameters
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* $support - [confidence](https://en.wikipedia.org/wiki/Association_rule_learning#Support), minimum relative amount of frequent item set in train sample
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* $confidence - [confidence](https://en.wikipedia.org/wiki/Association_rule_learning#Confidence), minimum relative amount of item set in frequent item sets
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```
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use Phpml\Association\Apriori;
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$associator = new Apriori($support = 0.5, $confidence = 0.5);
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```
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### Train
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To train a associator simply provide train samples and labels (as `array`). Example:
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```
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$samples = [['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta'], ['alpha', 'beta', 'epsilon'], ['alpha', 'beta', 'theta']];
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$labels = [];
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use Phpml\Association\Apriori;
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$associator = new Apriori($support = 0.5, $confidence = 0.5);
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$associator->train($samples, $labels);
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```
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You can train the associator using multiple data sets, predictions will be based on all the training data.
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### Predict
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To predict sample label use `predict` method. You can provide one sample or array of samples:
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```
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$associator->predict(['alpha','theta']);
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// return [['beta']]
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$associator->predict([['alpha','epsilon'],['beta','theta']]);
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// return [[['beta']], [['alpha']]]
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```
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### Associating
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Get generated association rules simply use `rules` method.
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```
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$associator->getRules();
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// return [['antecedent' => ['alpha', 'theta'], 'consequent' => ['beta'], 'support' => 1.0, 'confidence' => 1.0], ... ]
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```
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### Frequent item sets
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Generating k-length frequent item sets simply use `apriori` method.
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```
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$associator->apriori();
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// return [ 1 => [['alpha'], ['beta'], ['theta'], ['epsilon']], 2 => [...], ...]
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```
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