The efficient extraction of valuable patterns from large-scale datasets has become increasingly essential in various data mining applications. High Average-Utility Itemset Mining (HAUIM) is a crucial data mining task that involves the identification of itemsets with high utility values. This study comprehensively explored the current state of research in HAUIM, including its algorithms and features. The study discussed the significant advancements, challenges, and potential future directions in this field, providing valuable insights into the growing landscape of high-utility itemset mining.
Keywords
high average-utility itemset miningutility itemsetdata miningutilityhigh average-utility itemset
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