Associative classifier

An associative classifier (AC) is a kind of supervised learning model that uses association rules to assign a target value. The term associative classification was coined by Bing Liu et al. in , where the authors defined a model made of rules "whose right-hand side are restricted to the classification class attribute".

The model

The model generated by an AC and used to label new records consists of association rules, where the consequent corresponds to the class label. As such, they can also be seen as a list of "if-then" clauses: if the record matches some criteria (expressed in the left side of the rule, also called antecedent), it is then labeled accordingly to the class on the right side of the rule (or consequent).

Most of the ACs read the list of rules in order, and apply the first matching rule to label the new record, but several variants exist.

Metrics

The rules of an AC inherit some of the metrics of association rules, like the support or the confidence. These metrics can be used to order or filter the rules in the model, and to evaluate their quality.

Existing implementations

The first proposal of a classification model made of association rules was CBA.

Other notable implementations include:

• CMAR
• FOIL
• PRM
• CPAR