Associative classification is a combination of association rule mining and classification for prediction. For mining associative classification, traditional algorithms generate the complete set of association rules, and then use a minimum confidence threshold to select interesting rules for classification. If the number of association rules is very large, it is time consuming to select only the interesting rules. In this paper, a new algorithm, called TOPAC (Top Associative Classification), is proposed to solve the problem. The TOPAC algorithm directly produces the interesting rules without the generation of candidate rules. Moreover, it discovers the interesting rules based on frequent closed itemsets to reduce the redundancy rules.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.