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Issue title: Rough and Fuzzy Methods for Data Mining
Guest editors: A.E. Hassanienav, H. Sakaibw, D. Ślȩzakx, M.K. Chakrabortydy and W. Zhuz
Article type: Research Article
Authors: Sakai, Hiroshia; * | Okuma, Hitomib | Nakata, Michinoric | Ślȩzak, Dominikd; e
Affiliations: [a] Mathematical Sciences Section, Department of Basic Sciences, Faculty of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu 804, Japan | [b] Faculty of Education and Welfare Science, Oita University, Dannoharu, Oita 870, Japan | [c] Faculty of Management and Information Science, Josai International University, Gumyo, Togane, Chiba 283, Japan | [d] Institute of Mathematics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland | [e] Infobright Inc., Poland, Krzywickiego 34 pok. 219, 02-078 Warsaw, Poland | [v] Cairo University, Egypt | [w] Kyushu Institute of Technology, Japan | [x] University of Warsaw & Infobright Inc., Poland | [y] University of Calcutta, India | [z] UESTC, Chengdu, China
Correspondence: [*] Corresponding author. E-mail: [email protected]
Abstract: Rough Non-deterministic Information Analysis (RNIA) is a rough set-based data analysis framework for Non-deterministic Information Systems (NISs). RNIA-related algorithms and software tools developed so far for rule generation provide good characteristics of NISs and can be successfully applied to decision making based on non-deterministic data. In this paper, we extend RNIA by introducing stability factor that enables to evaluate rules in a more flexible way and by developing a question-answering functionality that enables decision makers to analyze data gathered in NISs in case there are no pre-extracted rules that may address specified conditions.
Keywords: Rough non-deterministic information analysis, Incomplete information, Rule generation, Apriori algorithm, Stability factor, Decision making
DOI: 10.3233/HIS-2011-0130
Journal: International Journal of Hybrid Intelligent Systems, vol. 8, no. 1, pp. 41-57, 2011
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