A frequent itemset generation approach in data mining using transaction-labelling dynamic itemset counting method
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
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.
International Journal of Data Mining, Modelling and Management;Volume;17;Issue;1;pp.54-74
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https://doi.org/10.1504/IJDMMM.2025.144615 https://www.scopus.com/pages/publications/86000134816?origin=resultslist
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