A study on interestingness measures for associative classifiers

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 supportconfidence 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. There are many other rule interestingness measures already used in machine learning, data mining and statistics. This work focuses on using 53 different objective measures for associative classification rules. A wide range of UCI datasets are used to study the impact of different "interestingness measures" on different phases of associative classifiers based on the number of rules generated and the accura...

• Type: Journal
• Year: 2010
• Where: SAC
• Authors: Mojdeh Jalali Heravi, Osmar R. Zaïane
• Keywords: Applied Computing, Associative Classification, Associative Classifiers, Interestingness Measures, SAC 2010