An efficient algorithm for mining high-utility itemsets with discount notion

Abstract

High-utility itemset mining has attracted significant attention from the research community. Identifying high-utility itemsets from a transaction database can help business owners to earn a better profit by promoting the sales of high-utility itemsets. The technique also finds applications in web-click stream analysis, biomedical data analysis,mobile E-commerce etc. Several algorithms have been proposed to mine highutility itemsets from a transaction database. However, these algorithms assume that items have a constant profit associated with them and don’t embed the notion of discount into the utility-mining framework. In this paper, we integrate the notion of discount in state-of-the-art utilitymining algorithms and propose an algorithm for efficiently mining highutility itemsets. We conduct extensive experiments on real and synthetic datasets and our results show that our proposed algorithm outperforms the state-of-the-art algorithms in terms of total execution time and number of itemsets that need to be explored.