Generation of Efficient Rules for Associative Classification

Abstract

Associative classification is a mining technique that integrates classification and association rule mining for classifying unseen data. Associative classification has been proved that it gives more accurate than traditional classifiers and generates useful rules which are easy to understand by a human. Due to inheriting from association rule mining, associative classification has to face a sensitive of minimum support threshold that a huge number of rules are generated when a low minimum support threshold is given. Some of the rules are not used for classification and need to be pruned. To eliminate unnecessary rules, this paper proposes a new algorithm to find efficient rules for classification. The proposed algorithm directly generates efficient rules. A vertical data representation technique is adopted to avoid the generation of unnecessary rules. Our experiments are conducted to compare the proposed algorithm with well-known algorithms, CBA and FACA. The experimental results show that the proposed algorithm is more accurate than CBA and FACA.