2026-07-28T21:37:11Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/5067 2022-03-24T23:16:46Z 1582963302567:1597824273898 user-ucsy

Analysis on Efficiency of Apriori and MBAT Algorithms Aung, Yu Nandar Zaw, Ei Phyu Data Mining is a fast developing field ofcomputer science and technology, which arehelpful to enable end users for decision makingprocess. One of the most important data miningprocesses is that of Association Rule Mining. Thispaper intends to the analysis on efficiency of thetwo algorithms (Apriori and MBAT) which findingfrequent itemsets in Association Rule Mining. TheAssociation Rule Mining is based mainly ondiscovering frequent itemsets. Apriori algorithmand other popular Association Rule Miningalgorithms mainly generate a large number ofcandidate items and scanning the database toomany times. To remove these deficiencies, thispaper presents a method named Matrix BasedFrequent Itemsets Minining algorithm with Tags(MBAT) which can reduce the number ofcandidate itemsets. In this paper, the system usedJava Programming Language with Follow Meproducts dataset to compare these two algorithms. 2017-12-27 http://hdl.handle.net/20.500.12678/0000005067 https://meral.edu.mm/records/5067