Associative Classification Based on Closed Frequent Itemsets

A new associative classification method named ACCF is presented based on the closed frequent itemsets. This method first mines all closed frequent itemsets and the candidate class association rules (CARs), and then constructs classifier based the selected CARs from the candidate CARS. The new instances are finally classified by a new way. Our theoretical analysis and substantial experiments on 18 datasets from UCI repository of machine learning databases show that ACCF is highly effective at classification of various kinds of datasets. Compared with the typical associative classification algorithms, ACCF can mine much less CARs and has higher average classification accuracy.