Associative classification aims to discover a set of constrained association rules, called Class Association Rules (CARs). The consequent of a CAR is a singleton and is restricted to be a class label. Traditionally, the classifier is built by selecting a subset of CARs based on some interestingness measure.
The proposed approach for associative classification, called Associative Classifier based on Closed Itemsets (ACCI), scans the dataset only once and generates a set of CARs based on closed itemsets (ClosedCARs) using a lattice based data structure. Subsequently, rule conflicts are removed and a subset of non-conflicting ClosedCARs which covers the entire training set is chosen as a classifier. The entire process is independent of the interestingness measure. Experimental results on benchmark datasets from UCI machine repository reveal that the achieved classifiers are more accurate than those built using existing approaches.