# Minimal decision rules based on the apriori algorithm

María Fernández; Ernestina Menasalvas; Óscar Marbán; José Peña; Socorro Millán

International Journal of Applied Mathematics and Computer Science (2001)

- Volume: 11, Issue: 3, page 691-704
- ISSN: 1641-876X

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topFernández, María, et al. "Minimal decision rules based on the apriori algorithm." International Journal of Applied Mathematics and Computer Science 11.3 (2001): 691-704. <http://eudml.org/doc/207527>.

@article{Fernández2001,

abstract = {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.},

author = {Fernández, María, Menasalvas, Ernestina, Marbán, Óscar, Peña, José, Millán, Socorro},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {association rules; minimal decision rules; Apriori algorithm; rough sets; rough dependencies; decision rules; rough set},

language = {eng},

number = {3},

pages = {691-704},

title = {Minimal decision rules based on the apriori algorithm},

url = {http://eudml.org/doc/207527},

volume = {11},

year = {2001},

}

TY - JOUR

AU - Fernández, María

AU - Menasalvas, Ernestina

AU - Marbán, Óscar

AU - Peña, José

AU - Millán, Socorro

TI - Minimal decision rules based on the apriori algorithm

JO - International Journal of Applied Mathematics and Computer Science

PY - 2001

VL - 11

IS - 3

SP - 691

EP - 704

AB - 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.

LA - eng

KW - association rules; minimal decision rules; Apriori algorithm; rough sets; rough dependencies; decision rules; rough set

UR - http://eudml.org/doc/207527

ER -

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