Mining local and peak high utility itemsets

A major limitation of traditional High Utility Itemset Mining (HUIM) algorithms is that they do not consider that the utility of itemsets may vary over time. Thus, traditional HUIM algorithms cannot find itemsets that do not yield a high utility when considering the whole database, but still have a high utility during specific time periods. Discovering such itemsets is useful, as a product may sell exceptionally well during specific time periods but not during the rest of the year. This paper addresses this limitation of HUIM by defining the problem of mining local high utility itemsets (LHUI), and an extension to mine peak high utility itemsets (PHUI), which consists of finding the time periods where an itemset generates a utility that is much higher than usual (a peak). Algorithms named LHUI-Miner and PHUI-Miner are proposed to mine these patterns. Moreover, because the set of PHUIs can be large and some items in PHUIs don't contribute much to their peaks, a third algorithm named NPHUI-Miner is proposed to discover a smaller set of patterns called Non-redundant Peak High Utility Itemsets (NPHUIs). Experimental results show that the proposed algorithms are efficient and can find useful patterns. (C) 2019 Elsevier Inc. All rights reserved.