LAUSR

High-utility itemset mining (HUIM) is a useful tool for analyzing customer behavior in the field of data mining. HUIM algorithms can discover the most beneficial itemsets in transaction databases, namely the high-utility itemsets (HUIs), in contrast to frequent itemset mining (FIM) algorithms that rely on detecting frequent patterns. Several algorithms have been proposed to effectively carry out this task, but most of them ignore the categorization of items. In many real-world transaction databases, this helpful information about the categories and subcategories of items, represented as a taxonomy, is useful. Therefore, traditional HUIM algorithms can only discover itemsets at the lowest level of abstraction and leave out several important patterns from higher levels. To address this limitation, this work suggests the use of items taxonomy. Besides, to further enhance the performance of the task several effective pruning techniques are also revised and utilized to tighten the search space when considering the taxonomy of items. To accurately find multi-level HUIs from transaction databases enhanced with taxonomy information, a new algorithm called MLHMiner (Multiple-Level HMiner) is proposed, which is an extended version of the HMiner algorithm. We also prove that the pruning techniques of HMiner can be applied in different abstraction levels to efficiently mine multi-level HUIs. It can be seen from the experimental evaluations on several databases (both real and synthetic) that the designed approach is capable of identifying useful patterns from different abstraction levels with high efficiency.