Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Logeswaran, K.; Suresh, P.; Savitha, S.; Anandamurugan, S. Title A name given to the resource Adaptive optimization with reinforcement learning for high utility itemset extraction Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Knowledge-Based Systems;Volume;331;Issue;;Article No.;114733; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.knosys.2025.114733" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.knosys.2025.114733</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020986688?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020986688?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Logeswaran K., Department of AI, ML and Data Science, School of Engineering and Technology, CHRIST University, Kengeri, Karnataka, Bangalore, 560074, India; Suresh P., Department of Database Systems, School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nadu, Vellore, 632 014, India; Savitha S., Department of Computer Science and Engineering, K.S.R College of Engineering, Tamil Nadu, Tiruchengode, 637215, India; Anandamurugan S., Department of Information Technology, Kongu Engineering College, Perundurai, Erode, Tamilnadu, 638060, India Description An account of the resource Extraction of High Utility Itemsets (HUI) plays a vital role in data mining that comprises several techniques developed to address it efficiently. However, when dealing with large itemsets and diverse items in a dataset, the problem's search space becomes notably complex and expansive. This makes the task of identifying HUIs more computationally expensive and time-consuming. In this paper, a novel Optimized Coverage list unit utilities-based High Utility Itemset (OCHUI) extraction approach is introduced for High Utility Itemset extraction. The extraction of high utility patterns and the extraction of qualified high utility itemsets are the two main processes in the suggested method. In the first step, high utility patterns are identified by mining metrics such as Redefined transaction-weighted utility, positive and negative Unit profit, Purchase quantity, and Coverage (RUPC) from the dataset. In the second step, qualified high utility itemsets are obtained optimally using an adaptive optimization algorithm called Cuckoo search Assisted Ant colony Optimization with Reinforcement Learning (CAAO-RL) is proposed. The Reinforcement Learning (RL) uses the On and Off policy method to intelligently leverage the tuning parameter of optimization. The RUPC model obtained the pattern score of 13600, runtime rate of 10.256 s and memory usage of 198 MB, respectively. 2025 Elsevier B.V. Subject The topic of the resource High utility itemset mining; Optimization algorithm; Optimized coverage list; Reinforcement learning; RUPC Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 9507051; CODEN: KNSYE Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online