TY  - JOUR
N1  - cited By 56
SP  - 2603
TI  - Algorithms for frequent itemset mining: a literature review
AV  - none
EP  - 2621
PB  - Springer Netherlands
SN  - 02692821
N2  - 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).
IS  - 4
ID  - scholars11087
KW  - Competition; Decision making
KW  -  Competitive advantage; Data analytics; Decision making process; Frequent itemset mining; Frequent pattern mining; Literature reviews; Memory consumption; Pattern analysis
KW  -  Data mining
JF  - Artificial Intelligence Review
A1  - Chee, C.-H.
A1  - Jaafar, J.
A1  - Aziz, I.A.
A1  - Hasan, M.H.
A1  - Yeoh, W.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044354882&doi=10.1007%2fs10462-018-9629-z&partnerID=40&md5=d5dab1484f453370f2c1f95dc2aadade
VL  - 52
Y1  - 2019///
ER  -