Rare Correlated Coherent Association Rule Mining With CLS-MMS

Abstract

Abstract The study of coherent association rules based on propositional logic is an important area of association rule mining. Users may get a large number of itemsets for low minsup and lose valuable itemsets for high minsup. Mining without minsup may cause itemset explosions that contain spurious itemsets with low correlations and take a long time to mine. For mining coherence rules, existing approaches consider only the frequent itemsets, ignoring rare itemsets. Moreover, all items in the database are regarded equally important, which is not practical in real-world applications. By using the confidence-lift specified multiple minimum supports combined with propositional logic, we propose an efficient approach called rare correlated coherent association rule mining that addresses all of the problems stated above. We define and incorporate termination bound of support (${s}_{TB}$) and termination bound of dissociation (${d}_{TB}$) for early pruning of the candidate itemsets. In the proposed approach, support thresholds are automatically applied to the itemsets and coherent association rules are derived from the frequent and rare itemsets with high correlation and confidence. Experimental results obtained from real-life datasets show the effectiveness of the proposed approach in terms of itemsets and rule generation, correlation, confidence, runtime and scalability.