Considering Re-occurring Features in Associative Classifiers
- Rafal Rak
- Wojciech Stach
- Osmar R. Zaiane, University of Alberta (Database)
- Maria-Luiza Antonie, Dept. of Computing Science UofA
There are numerous different classification methods; among the many we can cite associative classifiers. This newly suggested model uses association rule mining to generate classification rules associating observed features with class labels. Given the binary nature of association rules, these classification models do not take into account repetition of features when categorizing. In this paper, we enhance the idea of associative classifiers with associations with re-occurring items and show that this mixture produces a good model for classification when repetition of observed features is relevant in the data mining application at hand.
Citation
R. Rak, W. Stach, O. Zaiane, M. Antonie. "Considering Re-occurring Features in Associative Classifiers". Proceeding of the Pacific Asia Conference on Knowledge Discovery and Data Mining, pp 240-248, May 2005.| Keywords: | |
| Category: | In Conference |
| Web Links: | ACM Digital Library |
BibTeX
@incollection{Rak+al:PAKDD05,
author = {Rafal Rak and Wojciech Stach and Osmar R. Zaiane and Maria-Luiza
Antonie},
title = {Considering Re-occurring Features in Associative Classifiers},
Pages = {240-248},
booktitle = {Proceeding of the Pacific Asia Conference on Knowledge Discovery
and Data Mining},
year = 2005,
}Last Updated: January 31, 2020Submitted by Sabina P
