A lattice-based approach for mining high utility association rules

Most businesses focus on the profits. For example, supermarkets often analyze sale activities to investigate which products bring the most revenue, as well as find out customer trends based on their carts. To achieve this, a number of studies have examined high utility itemsets (HUI). Traditional association rule mining algorithms only generate a set of highly frequent rules, but these rules do not provide useful answers for what the high utility association rules are. Therefore, Sahoo et al. (2015) proposed an approach to generate utility-based non-redundant high utility association rules and a method for reconstructing all high utility association rules. This approach includes three phases: (1) mining high utility closed itemsets (HUCI) and generators; (2) generating high utility generic basic (HGB) association rules; and (3) mining all high utility association rules based on HGB. The third phase of this approach consumes more time when the HGB list is large and each rule in HGB has many items in both antecedent and consequent. To overcome this limitation, in this paper, we propose an algorithm for mining high utility association rules using a lattice. Our approach has two phases: (1) building a high utility itemsets lattice (HUIL) from a set of high utility itemsets; and (2) mining all high utility association rules (HARs) from the HUIL. The experimental results show that mining HARs using HUIL is more efficient than mining HARs from HGB (which is generated from HUCI and generators) in terms of runtime and memory usage.