eprintid: 11087
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/10/87
datestamp: 2023-11-10 03:25:38
lastmod: 2023-11-10 03:25:38
status_changed: 2023-11-10 01:14:28
type: article
metadata_visibility: show
creators_name: Chee, C.-H.
creators_name: Jaafar, J.
creators_name: Aziz, I.A.
creators_name: Hasan, M.H.
creators_name: Yeoh, W.
title: Algorithms for frequent itemset mining: a literature review
ispublished: pub
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
note: cited By 56
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).
date: 2019
publisher: Springer Netherlands
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044354882&doi=10.1007%2fs10462-018-9629-z&partnerID=40&md5=d5dab1484f453370f2c1f95dc2aadade
id_number: 10.1007/s10462-018-9629-z
full_text_status: none
publication: Artificial Intelligence Review
volume: 52
number: 4
pagerange: 2603-2621
refereed: TRUE
issn: 02692821
citation:   Chee, C.-H. and Jaafar, J. and Aziz, I.A. and Hasan, M.H. and Yeoh, W.  (2019) Algorithms for frequent itemset mining: a literature review.  Artificial Intelligence Review, 52 (4).  pp. 2603-2621.  ISSN 02692821