# 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 -

## References

top- Agrawal R., Imielinski T., Swami A. (1993): Mining association rules between sets of items in large databases. — Proc. ACM SIGMOD Int. Conf. Management of Data, Washington, pp.207–216.
- Bazan J.G. (1996): Dynamic reducts and statistical inference. — Proc. 5-th Int. Conf. Information Processing and Management of Uncertainty in Knowledge-Based Systems IPMU’96, Granada, Spain, pp.1147–1151.
- Bazan J.G. (1998): A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision tables, In: Rough Sets in Knowledge Discovery (Polkowski L. and Skowron A., Eds.). — Heidelberg: Physica-Verlag, pp.321–365. Zbl1067.68711
- Blake C.L. and Merz C.J. (2001): UCI Repository of machine learning databases. — Irvine, CA: University of California, Department of Information and Computer Science, http://www.ics.uci.edu/∼mlearn/MLRepository.html.
- Grzymala-Busse J.W. (1993): LERS: A system for learning from examples based on rough sets, In: Intelligent Decision Support: Handbook of Applicactions and Advances of Rough Set Theory (Slowinski R., Ed.). — Banff, Alberta: Kluwer Netherlands, pp.3–18.
- Choobineh F., Paule M., Silkker W. and Hashemei R. (1997): On integration of modified rough set and fuzzy logic as classifiers. — Proc. Joint Conf. Information Sciences, North Carolina, pp.255–258.
- Kosters W., Marchiori E. and Oerlemans A. (1999): Mining cluster with association rules. — Lecture Notes in Computer Science 1642, Springer, pp.39–50.
- Kryszkiewicz M. (1998a): String rules in large databases. — Proc. 7-th Int. Conf. Information Processing and Management of Uncertainty in Knowledge-Based Systems IPMU98, Paris, Vol.2, pp.1520–1527.
- Kryszkiewicz M. (1998b): Fast discovery of representative association rules. — Proc. 1-st Int. Conf. Rough Sets and Current Trends in Computing, RSCTC’98, Warsaw, Poland, pp.214–221.
- Kryszkiewicz M. and Rybinski H. (1998): Knowledge discovery from large databases using rough sets . — Proc. 6-th Europ. Congr. Intelligent Techniques and Soft Computing EUFIT’98, Aachen, Germany, Vol.1, pp.85–89.
- Lin T.Y. (1996): Rough set theory in very large databases. — Proc. Computational Enfineering in Systems Applications, CESA’96, Lille, France, Vol.2, pp.936–941.
- Michalski R., Carbonell J. and Mitchell T.M. (1986): Machine Learning: An Artificial Intelligence Approach, Vol. 1. — Palo Alto CA: Tioga Publishing. Zbl0593.68060
- Pawlak Z. (1991): Rough Sets—Theoretical Aspects of Reasoning about Data. — Dordrecht: Kluwer. Zbl0758.68054
- Quinlan J.R. (1986): Induction of decision trees. — Mach. Learn., Vol.1, pp.81–106.
- Shan N. (1995): Rule discovery from data using decision matrices. — M.Sc. Thesis, University of Regina.
- Shan N. and Ziarko W. (1994): An incremental learning algorithm for constructing decision rules, In: Rough Sets, Fuzzy Sets and Knowledge Discovery (W. Ziarko, Ed.). — Berlin: Springer, pp.326–334. Zbl0941.68698
- Shan N. and Ziarko W. (1995): Data-base acquisition and incremental modification of classification rules. — Comput. Intell., Vol.11, No.2, pp.357–369.
- Skowron A. (1995): Extracting laws from decision tables: A rough set approach. — Comput. Intell., Vol.11, No.2, pp.371–387.
- Stefanowski J. (1998): On rough set based approaches to induction of decision rules, In: Rough Sets in Knowledge Discovery (Polkowski L. and Skowron A., (Eds.). — Heidelberg: Physica-Verlag, pp.500–529. Zbl0927.68094
- Świniarski R. (1998a): Rough sets and Bayesian methods applied to cancer detection. — Proc. Rough Sets and Current Trends in Computing, RSCTC’98, Lecture Notes in Artificial Intelligence 1424, Berlin, pp.609–616.
- Świniarski R. (1998b): Rough sets and neural networks application to handwritten character recognition by complex Zernike moments. — Proc. Rough Sets and Current Trends in Computing, RSCTC’98, Lecture Notes in Artificial Intelligence 1424, Berlin, pp.616– 624.
- Ziarko W. (1993): The discovery, analysis, and representation of data dependencies in databases . — Proc. Knowledge Discovery in Databases, KDD-93, Washington, pp.195– 209.

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