A New Rough Sets Model Based on Database Systems

• Autorzy: Hu X. , Lin T.Y. , Han J.
• Języki publikacji: EN

Abstrakty

EN

Rough sets theory was proposed by Pawlak in the early 1980?s and has been applied successfully in a lot of domains. One of the major limitations of the traditional rough sets model in the real applications is the inefficiency in the computation of core and reduct, because all the intensive computational operations are performed in flat files. In order to improve the efficiency of computing core attributes and reducts, many novel approaches have been developed, some of which attempt to integrate database technologies. In this paper, we propose a new rough sets model and redefine the core attributes and reducts based on relational algebra to take advantages of the very efficient set-oriented database operations. With this new model and our new definitions, we present two new algorithms to calculate core attributes and reducts for feature selections. Since relational algebra operations have been efficiently implemented in most widely-used database systems, the algorithms presented in this paper can be extensively applied to these database systems and adapted to a wide range of real-life applications with very large data sets. Compared with the traditional rough set models, our model is very efficient and scalable.

Słowa kluczowe

EN

rough sets; database systems; relational algebra; reduct; feature selection

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2004
• Tom: Vol. 59, nr 2,3
• Strony: 135--152
• Opis fizyczny: Bibliogr. 21 poz., tab.

Twórcy

autor

Hu X.

• College of Information Science, Drexel University, Philadelphia, PA 19104, USA

autor

Lin T.Y.

• Department of Computer Science, San Jose State University, San Jose, CA 94403. USA

autor

Han J.

• Department of Computer Science, California State University Dominguez Hills, Carson, CA 90747, USA

Bibliografia

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• [5] Hu, X., Cercone N., Han, J., Ziarko. W.: GRS: A Generalized Rough Sets Model in Data Mining. In Lin, T.Y., Yao, Y.Y., Zadeh. L. eds. Data Mining. Rough Sets and Granular Computing, Physica-Verlag, 2002, 447-460
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• [19] Skowron. A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27(2-3) (1996) 245-253
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