A study on interestingness measures for associative classifiers
Mojdeh Jalali-Heravi, Osmar R. Zai͏̈ane · 2010
Associative classification is a rule-based approach to classify data relying on association rule mining by discovering associations between a set of features and a class label. Support and confidence are the de-facto "interestingness measures" used for discovering relevant association rules. The support-confidence framework has also been used in most, if not all, associative classifiers. Although support and confidence are appropriate measures for building a strong model in many cases, they are still not the ideal measures and other measures could be better suited.