relation: https://khub.utp.edu.my/scholars/11087/
title: Algorithms for frequent itemset mining: a literature review
creator: Chee, C.-H.
creator: Jaafar, J.
creator: Aziz, I.A.
creator: Hasan, M.H.
creator: Yeoh, W.
description: 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).
publisher: Springer Netherlands
date: 2019
type: Article
type: PeerReviewed
identifier:   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     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044354882&doi=10.1007%2fs10462-018-9629-z&partnerID=40&md5=d5dab1484f453370f2c1f95dc2aadade
relation: 10.1007/s10462-018-9629-z
identifier: 10.1007/s10462-018-9629-z