  Associative Classifier is a novel technique which is the integration of
Association Rule Mining and Classification. The difficult task in building
Associative Classifier model is the selection of relevant rules from a large
number of class association rules (CARs). A very popular method of ordering
rules for selection is based on confidence, support and antecedent size (CSA).
Other methods are based on hybrid orderings in which CSA method is combined
with other measures. In the present work, we study the effect of using
different interestingness measures of Association rules in CAR rule ordering
and selection for associative classifier.
