Updating mined class association rules for record insertion

Abstract

Mining class association rules is an interesting problem in classification and prediction. Some recent studies have shown that using classifiers based on class association rules resulted in higher accuracy than those obtained by using other classification algorithms such as C4.5 and ILA. Although many algorithms have been proposed for mining class association rules, they were used for batch processing. However, real-world datasets regularly change; thus, updating a set of rules is challenging. This paper proposes an incremental method for mining class association rules when records are inserted into the dataset. Firstly, a modified equivalence class rules tree (MECR-tree) is created from the original dataset. When records are inserted, nodes on the tree are updated by changing their information including Obidset (a set of object identifiers containing the node’s itemset), count, and pos. Secondly, the concept of pre-large itemsets is applied to avoid re-scanning the original dataset. Finally, a theorem is proposed to quickly prune nodes that cannot generate rules in the tree update process. Experimental results show that the proposed method is more effective than mining entire original and inserted datasets.