Efficient mining of succinct correlated high utility association rules

Abstract

This paper investigates the problem of mining high utility association rules from quantitative databases. Traditional approaches mainly rely on conventional utility-confidence measures without considering the actual strength of association among items. Due to the absence of a correlation measure, many discovered rules exhibit high utility while the items within the rules co-occur infrequently, making them difficult to interpret and of limited practical significance; moreover, the number of generated rules is often very large. To address these limitations, this paper proposes a novel utility-confidence measure and integrates the Bond correlation measure to ensure that the items in a rule not only yield high utility but also exhibit strong correlations. On this basis, the paper introduces a class of succinct correlated 
high utility association rules, called SCoHUARs, in which each rule is represented in a compact form that serves as a representative of an equivalence class of rules sharing the same support. In addition, a partitioning method for the set of SCoHUARs is presented to guarantee complete and non-redundant generation of all SCoHUARs, together with several optimization techniques to accelerate the mining process. Finally, the M-SCoHUAR algorithm is designed to efficiently mine SCoHUARs by integrating the proposed theoretical results. Experimental results demonstrate that the proposed algorithm significantly outperforms existing methods in terms of execution time, memory consumption, and the quality of the discovered patterns.