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Algorithms for frequent itemset mining: a literature review

Chee, C.-H. and Jaafar, J. and Aziz, I.A. and Hasan, M.H. and Yeoh, W. (2018) Algorithms for frequent itemset mining: a literature review. Artificial Intelligence Review . pp. 1-19.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Data Analytics plays an important role in the decision making process. Insights from such pattern analysis offer vast benefits, including increased revenue, cost cutting, and improved competitive advantage. However, the hidden patterns of the frequent itemsets become more time consuming to be mined when the amount of data increases over the time. Moreover, significant memory consumption is needed in mining the hidden patterns of the frequent itemsets due to a heavy computation by the algorithm. Therefore, an efficient algorithm is required to mine the hidden patterns of the frequent itemsets within a shorter run time and with less memory consumption while the volume of data increases over the time period. This paper reviews and presents a comparison of different algorithms for Frequent Pattern Mining (FPM) so that a more efficient FPM algorithm can be developed. © 2018 The Author(s)

Item Type:Article
Impact Factor:cited By 0; Article in Press
Uncontrolled Keywords:Competition; Decision making, Competitive advantage; Data analytics; Decision making process; Frequent itemset mining; Frequent pattern mining; Literature reviews; Memory consumption; Pattern analysis, Data mining
Departments / MOR / COE:Research Institutes > Institute for Autonomous Systems
ID Code:21676
Deposited By: Ahmad Suhairi
Deposited On:01 Aug 2018 02:09
Last Modified:10 Jan 2019 07:31

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