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2001 | 11 | 3 | 691-704
Tytuł artykułu

Minimal decision rules based on the apriori algorithm

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Tom
11
Numer
3
Strony
691-704
Opis fizyczny
Daty
wydano
2001
otrzymano
2001-03-01
poprawiono
2001-06-01
Twórcy
  • DLSIIS Facultad de Informatica, U.P.M., Madrid, Spain
  • DLSIIS Facultad de Informatica, U.P.M., Madrid, Spain
  • DLSIIS Facultad de Informatica, U.P.M., Madrid, Spain
  • DATSI Facultad de Informatica, U.P.M., Madrid, Spain
  • Universidad del Valle, Cali, Colombia
Bibliografia
  • 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.
  • 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.
  • Pawlak Z. (1991): Rough Sets—Theoretical Aspects of Reasoning about Data. — Dordrecht: Kluwer.
  • 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.
  • 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.
  • 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.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv11i3p691bwm
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