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Analyzing customer transactions to discover high-utility itemsets is a popular task, which consists of finding the sets of items that are purchased together and yield a high profit. However, many studies assume that transactional data is static while in real-life, it changes over time. For example, the unit profits of items may vary from one week to another because sale prices and production costs may change. Many algorithms for mining high-utility itemsets (HUI) ignore this important propertydoi:10.1109/access.2020.2992729 fatcat:sqx5bb3e7vbwfpdwts72wjqxga