Fastest Association Rule Mining Algorithm Predictor - FARM-AP
- Metanat Hooshsadat
- Hamman Samuel
- Sonal Patel
- Osmar R. Zaiane, University of Alberta (Database)
Association rule mining is a particularly well studied field in data mining given its importance as a building block in many data analytics tasks. Many studies have focused on efficiency because the data to be mined is typically very large. However, while there are many approaches in literature, each approach claims to be the fastest for some given dataset. In other words, there is no clear winner. On the other hand, there is panoply of algorithms and implementations specifically designed for parallel computing. These solutions are typically implementations of sequential algorithms in a multi processor configuration focusing on load balancing and data partitioning, each processor running the same implementation on it is own partition. The question we ask in this paper is whether there is a means to select the appropriate frequent itemset mining algorithm given a dataset and if each processor in a parallel implementation could select its own algorithm provided a given partition of the data.
Citation
M. Hooshsadat, H. Samuel, S. Patel, O. Zaiane. "Fastest Association Rule Mining Algorithm Predictor - FARM-AP". International C* Conference on Computer Science , Montreal, Canada, pp 43-50, May 2011.| Keywords: | data mining, association rules, classification |
| Category: | In Conference |
BibTeX
@incollection{Hooshsadat+al:11,
author = {Metanat Hooshsadat and Hamman Samuel and Sonal Patel and Osmar R.
Zaiane},
title = {Fastest Association Rule Mining Algorithm Predictor - FARM-AP},
Pages = {43-50},
booktitle = {International C* Conference on Computer Science },
year = 2011,
}Last Updated: January 14, 2020Submitted by Sabina P
