Based on rough set theory many algorithms for rules extraction from data have been proposed. Decision rules can be obtained directly from a database. Some condition values may be unnecessary in a decision rule produced directly from the database. Such values can then be eliminated to create a more comprehensible (minimal) rule. Most of the algorithms that have been proposed to calculate minimal rules are based on rough set theory or machine learning. In our approach, in a post-processing stage, we apply the Apriori algorithm to reduce the decision rules obtained through rough sets. The set of dependencies thus obtained will help us discover irrelevant attribute values.
@article{bwmeta1.element.bwnjournal-article-amcv11i3p691bwm,
author = {Fern\'andez, Mar\'\i a and Menasalvas, Ernestina and Marb\'an, \'Oscar and Pe\~na, Jos\'e and Mill\'an, Socorro},
title = {Minimal decision rules based on the apriori algorithm},
journal = {International Journal of Applied Mathematics and Computer Science},
volume = {11},
year = {2001},
pages = {691-704},
zbl = {1006.68133},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-amcv11i3p691bwm}
}
Fernández, María; Menasalvas, Ernestina; Marbán, Óscar; Peña, José; Millán, Socorro. Minimal decision rules based on the apriori algorithm. International Journal of Applied Mathematics and Computer Science, Tome 11 (2001) pp. 691-704. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-amcv11i3p691bwm/
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