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Scalability measurement of association rule mining algorithms
Joint Authors
Fagiri, Salam Uthman
Abd al-Salam, Muhammad Mirghani Muhammad
Uthman, Sayf al-Din Futuh
Source
Sudan Journal of Computing and Geoinformatics
Issue
Vol. 1, Issue 1 (31 Dec. 2017), pp.152-159, 8 p.
Publisher
Publication Date
2017-12-31
Country of Publication
Sudan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
In this paper, we evaluate the scalability of association rule mining algorithms in-terms of execution times and memory usage, we conducted the performance using two main association rules algorithms which exhibits best results among other reputed algorithms known as Apriori and FP-Growth algorithms.
The simulation has been conducted to measure two important factors: execution times and memory usage.
The obtained results showed that the FP-Growth outperforms Apriori algorithms achieving less execution time while Apriori maintaining low memory usage
American Psychological Association (APA)
Fagiri, Salam Uthman& Abd al-Salam, Muhammad Mirghani Muhammad& Uthman, Sayf al-Din Futuh. 2017. Scalability measurement of association rule mining algorithms. Sudan Journal of Computing and Geoinformatics،Vol. 1, no. 1, pp.152-159.
https://search.emarefa.net/detail/BIM-836655
Modern Language Association (MLA)
Fagiri, Salam Uthman…[et al.]. Scalability measurement of association rule mining algorithms. Sudan Journal of Computing and Geoinformatics Vol. 1, no. 1 (2017), pp.152-159.
https://search.emarefa.net/detail/BIM-836655
American Medical Association (AMA)
Fagiri, Salam Uthman& Abd al-Salam, Muhammad Mirghani Muhammad& Uthman, Sayf al-Din Futuh. Scalability measurement of association rule mining algorithms. Sudan Journal of Computing and Geoinformatics. 2017. Vol. 1, no. 1, pp.152-159.
https://search.emarefa.net/detail/BIM-836655
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 159
Record ID
BIM-836655