Skip to main content

Research Repository

See what's under the surface

Advanced Search

Frequent itemset mining in big data with effective single scan algorithms

Djenouri, Youcef; Djenouri, Djamel; Chun-Wei Lin, Jerry; Belhadi, Asma

Authors

Youcef Djenouri

Jerry Chun-Wei Lin

Asma Belhadi



Abstract

© 2013 IEEE. This paper considers frequent itemsets mining in transactional databases. It introduces a new accurate single scan approach for frequent itemset mining (SSFIM), a heuristic as an alternative approach (EA-SSFIM), as well as a parallel implementation on Hadoop clusters (MR-SSFIM). EA-SSFIM and MR-SSFIM target sparse and big databases, respectively. The proposed approach (in all its variants) requires only one scan to extract the candidate itemsets, and it has the advantage to generate a fixed number of candidate itemsets independently from the value of the minimum support. This accelerates the scan process compared with existing approaches while dealing with sparse and big databases. Numerical results show that SSFIM outperforms the state-of-the-art FIM approaches while dealing with medium and large databases. Moreover, EA-SSFIM provides similar performance as SSFIM while considerably reducing the runtime for large databases. The results also reveal the superiority of MR-SSFIM compared with the existing HPC-based solutions for FIM using sparse and big databases.

Journal Article Type Article
Publication Date Nov 9, 2018
Journal IEEE Access
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Volume 6
Pages 68013-68026
APA6 Citation Djenouri, Y., Djenouri, D., Chun-Wei Lin, J., & Belhadi, A. (2018). Frequent itemset mining in big data with effective single scan algorithms. IEEE Access, 6, 68013-68026. https://doi.org/10.1109/ACCESS.2018.2880275
DOI https://doi.org/10.1109/ACCESS.2018.2880275
Publisher URL https://doi.org/10.1109/ACCESS.2018.2880275

Files

Frequent Itemset Mining in Big Data With Effective Single Scan Algorithms (858 Kb)
PDF

Licence
http://www.rioxx.net/licenses/all-rights-reserved

Copyright Statement
(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.





You might also like



Downloadable Citations

;