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 Conference Paper Faculty Publications- Conference Papers 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 Kalpana, P.; Sumathi, P.; Jose, Teena; Deepa, S.; Gondkar, Raju Ramakrishna; Zeema, Loveline J. Title A name given to the resource Matrix-Based Apriori Methods for Frequent Pattern Mining: An In-Depth Survey 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 3rd International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2025 - Proceedings;pp.73-78 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICRAIS66073.2025.11234713" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICRAIS66073.2025.11234713</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029752804?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029752804?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 Kalpana P., Dept. of Computer Science, CHRIST University, Karnataka, Bangalore, India; Sumathi P., Dept. of Computer Science, Vysya College, Tamil Nadu, Salem, India; Jose T., Dept. of Computer Science, CHRIST University, Karnataka, Bangalore, India; Deepa S., Dept. of Computer Science, CHRIST University, Karnataka, Bangalore, India; Gondkar R.R., Dept. of Computer Science, CHRIST University, Karnataka, Bangalore, India; Zeema L.J., Dept. of Computer Science, CHRIST University, Karnataka, Bangalore, India Description An account of the resource Data Mining identifies intriguing, useful, and previously unknown patterns and correlations between data stored in databases or warehouses. Frequent Pattern Mining (FPM) is one of the vital methods in the prospering arena of data mining (DM), and it describes the relationship between the items in the datasets. In the last two decades, many studies were carried out in FPM using the Apriori algorithm. The Apriori algorithm requires many database scans and produces numerous candidate itemsets, increasing I/O cost and decreasing computational efficiency. To address these issues, researchers contributed many improved versions of Apriori and proved that those algorithms scan the database only once and identify the frequent itemsets quickly, especially when the itemsets are higher, and provide higher efficiency and feasibility. This research article summarizes matrix-based Apriori algorithms in the literature used for identifying frequent itemsets. 2025 IEEE. Subject The topic of the resource Apriori; Data Mining; Frequent Pattern Mining; Matrix-based Apriori Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150143-3; Language A language of the resource English Type The nature or genre of the resource Conference paper 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