Abstract
High-utility itemset mining (HUIM), which is the detection of high-utility itemsets (HUIs) in a transactional database, provides the decision maker with greaterfiexibility to exploit item utilities, such as quantity and profits, to extract remarkable and efficient database patterns. However, most prevailing empirical articles have focused on HUIs. Nevertheless, in many practical situations, low-utility itemsets (LUIs) maintain a high level of significance and usage (e.g., in security systems and the low-utility itemsets represent the security system vulnerabilities that need monitoring). Hence, this paper proposes a new association rule mining (ARM) framework named low-utility itemset mining (LUIM) that extracts LUIs. Enhancing the performance of LUIM, the researchers here propose innovative HUI generators, determining the generators based on the itemset transaction weight utility (TWU) rather than the support values used in HUG-Miner and GHUI-Miner. Moreover, this paper offers two efficient algorithms called LUG-Miner and LUIMA. The LUG-Miner extracts high and low-utility generators while LUIMA extracts low-utility itemsets using low-utility generators (LUGs). The experimental results on both dense and sparse datasets illuminated the recommended framework, and the algorithms are efficiently operational.