Abstract
We propose a new computation model for rough set theory using relational algebra operations in this paper. We present the necessary and sufficient conditions on data tables under which an attribute is a core attribute and those under which a subset of condition attributes is a reduct, respectively. With this model, two algorithms for core attributes computation and reduct generation are suggested. The correctness of both algorithms is proved and their time complexity is analyzed. Since relational algebra operations have been efficiently implemented in most widely-used database systems, the algorithms presented can be extensively applied to these database systems and adapted to a wide range of real-life applications with very large data sets.
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References
Bell, D., Guan, J.: Computational methods for rough classification and discovery. J. of ASIS 49(5), 403–414 (1998)
Cercone, N., Ziarko, W., Hu, X.: Rule Discovery from Databases: A Decision Matrix Approach. In: Proc. Int’l Sym. on Methodologies for Intelligent System (1996)
Deogun, J., Choubey, S., Taghavan, V., Sever, H.: Feature selection and effective classifiers. J. of ASIS 49(5), 423–434 (1998)
Hu, X., Lin, T.Y., Han, J.: A New Rough Sets Model Based on Database Systems. In: Proc. of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (2003)
Garcia-Molina, H., Ullman, J.D., Widom, J.: Database System Implementation. Prentice Hall, Englewood Cliffs (2000)
Kumar, A.: A New Technique for Data Reduction in A Database System for Knowledge Discovery Applications. J. of Intelligent Systems 10(3)
Lin, T.Y., Cercone, N.: Applications of Rough Sets Theory and Data Mining. Kluwer Academic Publishers, Dordrecht (1997)
Lin, T.Y., Yao, Y.Y., Zadeh, L.A.: Data Mining, Rough Sets and Granular Computing. Physical-Verlag, Heidelberg (2002)
Modrzejewski, M.: Feature Selection Using Rough Sets Theory. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 213–226. Springer, Heidelberg (1993)
Pawlak, Z.: Rough Sets. International Journal of Information and Computer Science 11(5), 341–356 (1982)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1991)
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Han, J., Hu, X., Lin, T.Y. (2003). A New Computation Model for Rough Set Theory Based on Database Systems. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2003. Lecture Notes in Computer Science, vol 2737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45228-7_38
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DOI: https://doi.org/10.1007/978-3-540-45228-7_38
Publisher Name: Springer, Berlin, Heidelberg
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