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Discovering high-utility itemsets from a transaction database is one of the important tasks in High-Utility Itemset Mining (HUIM). The discovered high-utility itemsets (HUIs) must meet a user-defined given minimum utility threshold. Several methods have been proposed to solve the problem efficiently. However, they focused on exploring and discovering the set of HUIs. This research proposes a more generalized approach to mine HUIs using any user-specified correlated measure, named the General Method for Correlated High-utility itemset Mining (GMCHM). This proposed approach has the ability to discover HUIs that are highly correlated, based on the all_confidence and bond measures (and 38 other correlated measures). Evaluations were carried out on the standard datasets for HUIM, such as Accidents, BMS_utility and Connect. The results proved the high effectiveness of GMCHM in terms of running time, memory usage and the number of scanned candidates.

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