2006, Volume 8, Issue 7
Strategic Study of CAE >> 2006, Volume 8, Issue 7
Incomplete Fuzzy Information System
1. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China
2. School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, jiangsu 212003, China
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Abstract
In this paper, incomplete fuzzy information system is studied. In order to deal with it by rough set theory, fuzzy compatible relation and fuzzy rough approximation are defined by logic function. Furthermore, fuzzy covering on universe is proposed, three different operations on the coverings are formed and then some significant results are gained. Besides, with the two new definitions of fuzzy rough entropies, uncertain factors could be effectively measured, some important relationships between the varieties of uncertain factors and the strength of those entropies are discussed carefully. Finally, knowledge dependency in rough set theory is transformed to fuzzy knowledge dependency in the incomplete fuzzy information system. A new method for measuring the strength of partial fuzzy knowledge dependency is proposed. Some immediate theorems are proved.
Keywords
incomplete fuzzy information system ; fuzzy compatible relation ; fuzzy rough set ; fuzzy covering ; fuzzy rough entropy ; fuzzy knowledge dependency
References
[ 1 ] Pawlak Z.Rough set theory and its applications to dataanalysis[J].Journal of Cybernetics and Systems, 1998, 29:661~688
[ 2 ] Pawlak Z.Rough sets and intelligent data analysis[J].Journal of Information Sciences, 2002, 147:1~12
[ 3 ] Mieszkowicz-Rolka A, Rolka L.Fuzziness in informationsystems[J].Electronic Notes in Theoretical ComputerScience, 2003, 82 (4) :1~10
[ 4 ] Kryszkiewicz M.Rough set approach to incompleteinformation systems[J].Journal of Information Sciences, 1998, 112:39~49
[ 5 ] Stefanowski J.Incomplete information tables and roughclassification[J].Journal of Computational Intelligence, 2001, 17 (3) :545~566
[ 6 ] 王国胤.Rough集理论在不完备信息系统中的扩充[J].计算机研究与发展, 2002, 39 (10) :1238~1243 link1
[ 7 ] 张铃, 张钹.模糊商空间理论 (模糊粒度计算方法) [J].软件学报, 2003, 14 (4) :770~776 link1
[ 8 ] Wu Weizhi, Zhang Wenxiu, Li Huaizu.Knowledgeacquisition in incomplete fuzzy information systems via therough set approach[J].Expert Systems, 2003, 20 (5) :280~286
[ 9 ] 祝峰, 王飞跃.关于广义覆盖粗集的一些基本结果[J].模式识别与人工智能, 2002, 15 (1) :6~13
[10] Liang Jiye, Shi Zhongzhi.The information entropy, roughentropy and knowledge granulation in rough set theory[J].International Journal of Uncertainty, Fuzziness andKnowledge Based Systems, 2004, 12 (1) :37~46
[11] Li Qiang, Li Jianhua, Li Xiang, et al.Evaluationincompleteness of knowledge in data mining[A].InAdvanced Workshop on Content Computing, 2004, LectureNotes in Computer Science 3309[C].Berlin, Springer-Verlag, 2004.278~284
[12] Tyagia B K, Sharfuddinb A, Dutta R N, et al.Acomplete axiomatization of fuzzy functional dependenciesusing fuzzy function[J].Fuzzy Sets and Systems, 2005, 151:363~379
[13] Yahia S B, Ounalli H, Jaoua A.An extension of classicalfunctional dependency:dynamic fuzzy functionaldependency[J].Journal of Information Sciences, 1999, 119:219~234
[14] Sozat M I, Yazici A.A complete axiomatization for fuzzyfunctional and multivalued dependencies in fuzzy databaserelations[J].Fuzzy Sets and Systems, 2001, 117:161~181
[15] Radzikowska A M, Kerre E E.A comparative study offuzzy and rough sets[J].Fuzzy Sets and Systems, 2002, 126:137~155
[16] Yao Yiyu.Probabilistic approaches to rough sets[J].Expert Systems, 2003, 20:287~297
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