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Frequent itemsets mining using random walks for record insertion and deletion
Date Issued
February 23, 2017
Type
Conference Paper
Abstract
In 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.
Citation
Proceedings of 2016 8th International Conference on Information Technology and Electrical Engineering Empowering Technology for Better Future Icitee 2016, 2017