Classification and variable selection using the mining of positive and negative association rules

Association rules (ARs) have been applied to classification and variable selection. However, currently, only positive ARs are used for variable selection, while only special forms of positive and negative association rules (PNARs) are used for classification. The purpose of this work was to investigate variable selection and classification methods by mining another, more general form of PNARs, one that is more suitable for binary classification and variable selection problems. The algorithm for mining such PNARs exploits the downward closure property of negative itemsets. It is built based solely on items in a transactional database and on equivalence classes under the support-confidence framework. The algorithm combines the process of mining frequent itemsets and rule generation and is both sound and complete. Experimental results on 10 binary datasets of the variable selection and classification methods using the PNARs mined by the proposed algorithm show that these methods are superior to variable selection methods that use the mutual information measure and the chi-squared test and 10 popular classification algorithms, respectively.