Abstract
Starting point of rough set based data analysis is a data set, called an information system, whose columns are labeled by attributes, rows are labeled by objects of interest and entries of the table are attribute values. In fact, hierarchical attribute values exists impliedly in many real-world applications, but it has seldom been taken into consideration in traditional rough set theory and its extensions. In this paper, each attribute in an information system is generalized to a concept hierarchy tree by considering hierarchical attribute values. A hierarchical information system is obtained, it is induced by generalizing a given flat table to multiple data tables with different degrees of abstraction, which can be organized as a lattice. Moreover, we can choose any level for any attribute according to the need of problem solving, thus we can discovery knowledge from different levels of abstraction. Hierarchical information system can process data from multilevel and multiview authentically.
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References
Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Ziarko, W.: Variable precision rough set model. Journal of computer and system sciences 46(1), 39–59 (1993)
Slezak, D., Ziarko, W.: Variable precision Bayesian rough set model. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 312–315. Springer, Heidelberg (2003)
Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. International journal of general systems 17, 191–209 (1990)
Ziarko, W.: Probabilistic rough sets. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 283–293. Springer, Heidelberg (2005)
Yao, Y.Y.: Probabilistic rough set approximations. International Journal of Approximate Reasoning 49(2), 255–271 (2008)
Hu, X., Cercone, N.: Discovery maximal generalized decision rules through horizontal and vertical data reduction. Computational Intelligence 17(4), 685–702 (2001)
Shan, N., Hamilton, H.J., Cercone, N.: GRG:Knowledge Discovery Using Information Generalization, Information Reduction, and Rule Generation. In: Proceedings of the seventh international conference on tools with artificial intelligence, Washington, DC, USA, pp. 372–379. IEEE Computer Society, Los Alamitos (1995)
Hong, T.-P., Lin, C.-E., Lin, J.-H., Wang, s.-L.: Learning cross-level certain and possible rules by rough sets. Expert Systems with Applications 34(3), 1698–1706 (2008)
Yao, Y.Y.: Stratified rough sets and granular computing. In: Proceedings of the 18th International Conference of the North American on Fuzzy Information Processing Society, pp. 800–804 (1999)
Yao, Y.Y.: Information Granulation and Rough Set Approximation. International Journal of Intelligent Systems 16(1), 87–104 (2001)
Wang, Q., Wang, X., Zhao, M., Wang, D.: Conceptual hierarcy based rough set model. In: Proceedings of the second of International Conference on Machine Learning and Cybernetics, pp. 402–406 (2003)
Feng, Q., Miao, D., Cheng, Y.: Hierarchical decision rules mining. Expert systems with applications 37(3), 2081–2091 (2010)
Bargiela, A., Pedrycz, W.: The roots of granular computing. In: Proceedings of the 2006 IEEE international conference on granular computing, pp. 806–809 (2006)
Wille, R.: Concept lattices and conceptual knowledge systems. Computers & Mathematics with Applications 23(6-9), 493–515 (1992)
Yi, H.Y., de la Iglesia, B., Rayward-Smith, V.J.: Using Concept Taxonomies for Effective Tree Induction. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y.-m., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005, Part II. LNCS (LNAI), vol. 3802, pp. 1011–1016. Springer, Heidelberg (2005)
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Feng, Q. (2010). Hierarchical Information System and Its Properties. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds) Rough Set and Knowledge Technology. RSKT 2010. Lecture Notes in Computer Science(), vol 6401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16248-0_72
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DOI: https://doi.org/10.1007/978-3-642-16248-0_72
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