Fast Parallel Association Rule Mining Without Candidacy Generation
- Osmar R. Zaiane, University of Alberta (Database)
- Mohammad El-Hajj
- Paul Lu, Department of Computing Science
In this paper we introduce a new parallel algorithm MLFPT (Multiple Local Frequent Pattern Tree) [11] for parallel mining of frequent patterns, based on FP-growth mining, that uses only two full I/O scans of the database, eliminating the need for generating the candidate items and distributing the work fairly among processors. We have devised partitioning strategies at different stages of the mining process to achieve near optimal balancing between processors.We have successfully tested our algorithm on datasets larger than 50 million transactions.
Citation
O. Zaiane, M. El-Hajj, P. Lu. "Fast Parallel Association Rule Mining Without Candidacy Generation". IEEE International Conference on Data Mining (ICDM), San Jose, USA, (ed: Nick Cercone, Tsau Young Lin, Xindong Wu), pp 665-668, November 2001.| Keywords: | |
| Category: | In Conference |
| Web Links: | ACM Digital Library |
BibTeX
@incollection{Zaiane+al:ICDM01,
author = {Osmar R. Zaiane and Mohammad El-Hajj and Paul Lu},
title = {Fast Parallel Association Rule Mining Without Candidacy Generation},
Editor = {Nick Cercone, Tsau Young Lin, Xindong Wu},
Pages = {665-668},
booktitle = {IEEE International Conference on Data Mining (ICDM)},
year = 2001,
}Last Updated: February 04, 2020Submitted by Sabina P
