A New Algorithm for High Average-utility Itemset Mining

High utility itemset mining (HUIM) is a new emerging field in data mining, which has gained growing interests due to its various applications. The goal of this work is to discover all itemsets whose utility exceeds minimum threshold. The basic HUIM problem does not consider length of itemsets in its utility measurement and the utility values tend to become higher for itemsets containing more items. Hence, HUIM algorithms discover a huge enormous number of long patterns. High average-utility itemset mining (HAUIM) is a variation in HUIM that selects patterns by considering both their utilities and lengths. In the last decades, several algorithms have been introduced to mine high average-utility itemsets. To speed up the HAUIM process, here, a new algorithm is proposed, which uses a new list structure and pruning strategy. Several experiments performed on the real and synthetic datasets show that the proposed algorithm outperforms the state-of-the-art HAUIM algorithms in terms of runtime and memory consumption.