Considering re-occurring features in associative classifiers

Authors:
Rafal Rak;Wojciech Stach;Osmar R. Zaïane;Maria-Luiza Antonie
Affiliations:
University of Alberta, Edmonton, Canada;University of Alberta, Edmonton, Canada;University of Alberta, Edmonton, Canada;University of Alberta, Edmonton, Canada
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

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Abstract

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.