Frequent itemsets mining using random walks for record insertion and deletion

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dc.contributor.authorThusaranon, Panita
dc.contributor.authorKreesuradej, Worapoj
dc.date.accessioned2026-08-06T10:16:15Z
dc.date.available2026-08-06T10:16:15Z
dc.date.issued2017-02-23
dc.description.abstractIn Association rules mining, the task of finding frequent itemsets in dynamic database is very important because the updates may not only invalidate some existing rules but also make other rules relevant. In this paper, we propose a new algorithm to maintain frequent itemsets of a dynamic database in the case of record insertion as well as deletion simultaneously. Basically, the proposed algorithm maintains not only the support counts of frequent itemsets but also the support counts of prospective frequent itemsets, i.e., infrequent itemsets that promise to be frequent in the future, in an original database. Prospective frequent itemsets, which are obtained by using the principle of Random Walks, can help to reduce a number of times to rescan the original database.
dc.identifier.citationProceedings of 2016 8th International Conference on Information Technology and Electrical Engineering Empowering Technology for Better Future Icitee 2016, 2017
dc.identifier.doi10.1109/ICITEED.2016.7863273
dc.identifier.other2-s2.0-85016068122
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/7538
dc.sourceProceedings of 2016 8th International Conference on Information Technology and Electrical Engineering Empowering Technology for Better Future Icitee 2016
dc.subjectdata mining
dc.subjectfrequent itemsets mining
dc.subjectincremental association rule mining
dc.subjectprospective frequent itemsets
dc.subjectrandom walks
dc.titleFrequent itemsets mining using random walks for record insertion and deletion
dc.typeConference Paper

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