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

61 lines
1.8 KiB
Markdown
Raw Normal View History

# Apriori Associator
Association rule learning based on [Apriori algorithm](https://en.wikipedia.org/wiki/Apriori_algorithm) for frequent item set mining.
### Constructor Parameters
* $support - [confidence](https://en.wikipedia.org/wiki/Association_rule_learning#Support), minimum relative amount of frequent item set in train sample
* $confidence - [confidence](https://en.wikipedia.org/wiki/Association_rule_learning#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);
```
You can train the associator using multiple data sets, predictions will be based on all the training data.
### 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 => [...], ...]
```