Abstract
We consider approximate versions of fundamental notions of theories of rough sets and association rules. We analyze the complexity of searching for α-reducts, understood as subsets discerning “α-almost” objects from different decision classes, in decision tables. We present how optimal approximate association rules can be derived from data by using heuristics for searching for minimal α-reducts. NP-hardness of the problem of finding optimal approximate association rules is shown as well. It makes the results enabling the usage of rough sets algorithms to the search of association rules extremely important in view of applications.
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References
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of assocation rules. In: Fayad, V.M., Piatetsky Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advanced in Knowledge Discovery and Data Mining, pp. 307–328. AAAI/MIT Press (1996)
Bazan, J.: A comparison of dynamic non-dynamic rough set methods for extracting laws from decision tables. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery. Methodology and Applications, vol. 1, pp. 321–365. Physica-Verlag, Heidelberg (1998)
Nguyen, H.S., Nguyen, S.H.: Pattern extraction from data. Fundamenta Informaticae 34, 129–144 (1998)
Nguyen, H.S., Skowron, A., Synak, P.: Discovery of data pattern with applications to decomposition and classification problems. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery, vol. 2, pp. 55–97. Physica-Verlag, Heidelberg (1998)
Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Skowron, A.: Synthesis of adaptive decision systems from experimental data. In: Aamodt, A., Komorowski, J. (eds.) Proc. of the Fifth Scandinavian Conference on Artificial Intelligence (SCAI 1995), Trondheim, Norway, pp. 220–238. IOS Press, Amsterdam (1995)
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Słowiński, R. (ed.) Intelligent decision support: Handbook of applications and advances of the rough sets theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)
Ślęzak, D.: Decision information functions for inconsistent decision tables analysis. Accepted to International Conference EUFIT (1999)
Ślęzak, D.: Reasoning in decision tables with frequency based implicants (in preparation)
Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New Parallel Algorithms for Fast Discovery of Association Rules. Special issue on Scalable High-Performance Computing for KDD, Data Mining and Knowledge Discovery: An International Journal 1(4), 343–373 (1997)
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Nguyen, H.S., Ślęzak, D. (1999). Approximate Reducts and Association Rules. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_18
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DOI: https://doi.org/10.1007/978-3-540-48061-7_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66645-5
Online ISBN: 978-3-540-48061-7
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