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 Balaram, Ambily; Raju, Nedunchezhian Title A name given to the resource A frequent itemset generation approach in data mining using transaction-labelling dynamic itemset counting method 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 International Journal of Data Mining, Modelling and Management;Volume;17;Issue;1;pp.54-74 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1504/IJDMMM.2025.144615" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJDMMM.2025.144615</a> <br /><br /><a href="https://www.scopus.com/pages/publications/86000134816?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/86000134816?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 Balaram A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Banglore, India; Raju N., Department of Computer Science and Engineering, Coimbatore Institute of Technology, Tamil Nadu, Coimbatore, India Description An account of the resource A significant amount of data is generated, gathered, stored, and evaluated in real-world applications as a result of technology breakthroughs. Data mining (DM) combines a number of disciplines to efficiently discover hidden patterns from vast archives of historical information. To significantly reduce complexities associated with data, the proposed method, transaction-labelling dynamic itemset counting (TL-DIC), utilises a labelling approach on the given transactional database to logically arrange and process the underlying transactions. This method generates frequent itemsets thereby improving the performance of conventional dynamic itemset counting (DIC) method. Based on experimental findings, the average scan count in DIC and M-Apriori is 4% and 3.66%, respectively higher than TL-DIC, for different support counts. TL-DIC executes 20% and 16% quicker than DIC and M-Apriori, respectively, in terms of execution time. These results validate the proposed approachs efficacy in creating frequent itemsets from large datasets. Copyright 2025 Inderscience Enterprises Ltd. Subject The topic of the resource ARM; association rule mining; complexities; data mining; DIC; dynamic itemset counting method; frequent itemset generation; labelling; minimum support threshold; scan count; TL; TL-DIC; transaction labelling; transaction-labelling dynamic itemset counting; transactional database Publisher An entity responsible for making the resource available Inderscience Publishers Relation A related resource ISSN: 17591163; 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