Mutalib, Sofianita and Abdul Subar, Ammar Azri and Abdul Rahman, Shuzlina and Mohamed, Azlinah (2016) Comparative study of apriori-variant algorithms. In: Knowledge Management International Conference (KMICe) 2016, 29 – 30 August 2016, Chiang Mai, Thailand.

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Abstract

Big Data era is currently generating tremendous amount of data in various fields such as finance, social media, transportation and medicine. Handling and processing this “big data” demand powerful data mining methods and analysis tools that can turn data into useful knowledge. One of data mining methods is frequent itemset mining that has been implemented in real world applications, such as identifying buying patterns in grocery and online customers’ behavior.Apriori is a classical algorithm in frequent itemset mining, that able to discover large number or itemset with a certain threshold value. However, the algorithm suffers from scanning time problem while generating candidates of frequent itemsets.This study presents a comparative study between several Apriori-variant algorithms and examines their scanning time.We performed experiments using several sets of different transactional data.The result shows that the improved Apriori algorithm manage to produce itemsets faster than the original Apriori algorithm.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	ISBN: 978-967-0910-19-2 Organized by: College Art and Sciences, Universiti Utara Malaysia
Uncontrolled Keywords:	Apriori, Association Rule Mining, Frequent Itemset Mining
Subjects:	Q Science > QA Mathematics
Divisions:	School of Computing
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	29 Nov 2016 02:50
Last Modified:	01 Nov 2020 08:11
URI:	https://repo.uum.edu.my/id/eprint/20082

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