Effective algorithms to mine skyline frequent-utility itemsets

Abstract

Skyline frequent-utility itemset mining is used to discover itemsets that are non-dominated by considering both support and utility factors. It is an extension of high-utility itemset mining. Most existing algorithms are based on the utility-list structure to mine skyline frequent-utility itemsets. A major limitation of utility-list based algorithms is that numerous join operations consume a huge amount of time and memory. To address this issue, two algorithms named EMSFUI-D and EMSFUI-B are proposed to mine skyline frequent-utility itemsets. EMSFUI-D performs the depth-first search to explore the search space of all itemsets. EMSFUI-B discovers itemsets based on the breadth-first search. Both algorithms utilize two pruning strategies to limit the search space. Moreover, in order to further facilitate the mining performance, the ISU-1 and ISU-2 structures are presented in EMSFUI-D to provide tighter utility upper bounds. These structures maintain the support and utility information of all 1-itemsets and 2-itemsets, respectively. Thus, there is no need to use these structures to prune search space in the breadth-first search algorithm. An extensive experimental study on real and synthetic datasets shows that our proposed algorithms outperform the state-of-the-art SKYFUP-D and SKYFUP-B algorithms in terms of execution time, memory consumption and pruning performance. Moreover, our designed algorithms are scalable for handling a large number of distinct items and transactions.