Difference relation-based rough set and negative rules in incomplete information system

International Journal of Uncertainty, Fuzziness and Knowldege-Based Systems

Volumn 17, Issue 5, 2009, Pages 649-665

  Yang, Xibei ^a,b   Yu, Dongjun ^a   Yang, Jingyu ^a   Song, Xiaoning ^a  

^a NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b SAN JOSE STATE UNIVERSITY   (United States)

Author keywords

Difference relation; Incomplete information system; Knowledge reduction; Negative rules; Rough set

Indexed keywords

APPROXIMATION ALGORITHMS; INFORMATION SYSTEMS; INFORMATION USE;

ATTRIBUTE-VALUE PAIRS; BINARY RELATION; CONDITION; DIFFERENCE RELATION; INCOMPLETE INFORMATION SYSTEMS; KNOWLEDGE REDUCTION; NEGATIVE RULE; RELATION-BASED; ROUGH SET; ROUGH SET MODELS;

ROUGH SET THEORY;

EID: 70350031311     PISSN: 02184885     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218488509006194     Document Type: Article

References (28)

1. Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning About Data (Kluwer Academic Publishers, 1991).
2. Z. Pawlak, Rough set theory and its applications to data analysis, Cybernet. Syst. 29 (1998) 661-688.
3. Z. Pawlak, Rough sets and intelligent data analysis, Inform. Sci. 147 (2002) 1-12.
4. Z. Pawlak and A. Skowron, Rudiments of rough sets, Inform. Sci. 177 (2007) 3-27.
5. Z. Pawlak and A. Skowron, Rough sets: Some extensions, Inform. Sci. 177 (2007) 28-40.
6. Z. Pawlak and A. Skowron, Rough sets and Boolean reasoning, Inform. Sci. 177 (2007) 41-73.
7. S. Greco, B. Matarazzo and R. S lowinski, Handing missing values in rough set analysis of multi-attribute and multi-criteria decision problems, New Directions in Rough Sets, Data Mining and Granular-Soft Somputing, 7th Int. Workshop, RSFDGrC '99, eds. N. Zhong, A. Skowron and S. Ohsuga, Yamaguchi, Japan, November 9-11, 1999, pp. 146-157.
8. J. W. Grzymala-Busse and A. Y.Wang, Modified algorithms LEM1 and LEM2 for rule induction from data with missing attribute values, Proc. 5th Int. Workshop on Rough Sets and Soft Computing at the Third Joint Conf. Information Sciences, Research Triangle Park, NC, 1997, pp. 69-72.
9. J. W. Grzymala-Busse, Data with missing attribute values: Generalization of indis-cernibility relation and rule reduction, Lecture Notes in Computer Science 3100 (2004) 78-95.
10. J. W. Grzymala-Busse, Characteristic relations for incomplete data: A generalization of the indiscernibility relation, in: S. Tsumoto et al. (Eds.), Proc. Third Int. Conf. on Rough Sets and Current Trends in Computing, eds, S. Tsumoto et al., New York, 2004, pp. 244-253.
11. B. Huang, L. Guo, X. He and X. Z. Zhou, Rough computation based on similarity matrix, Lecture Notes in Arti cial Intelligence 3613 (2005) pp. 1223-1231.
12. M. Kryszkiewicz, Rough set approach to incomplete information systems, Inform. Sci. 112 (1998) 39-49.
13. M. Kryszkiewicz, Rule in incomplete information systems, Inform. Sci. 113 (1999) 271-292.
14. Y. Leung and D. Y. Li, Maximal consistent block technique for rule acquisition in incomplete information systems, Inform. Sci. 115 (2003) 85-106.
15. Y. Leung, W. Z. Wu and W. X. Zhang, Knowledge acquisition in incomplete infor-mation systems: A rough set approach, Eur. J. Oper. Res. 168 (2006) 164-180.
16. J. Liang, Z. Shi, D. Li and M. J. Wierman, Information entropy, rough entropy and knowledge granulation in incomplete information systems, Int. J. Gen. Syst. 35 (2006) 641-654. (Pubitemid 44728618)
17. J. Y. Liang and Z. B. Xu, The algorithm on knowledge reduction in incomplete information systems, Int. J. Uncertain. Fuzz. 10 (2002) 95-103.
18. P. J. Lingras and Y. Y. Yao, Data mining using extensions of the rough set model, J. Am. Soc. Inf. Sci. Tec. 49 (1998) 415-422.
19. M. W. Shao and W. X. Zhang, Dominance relation and rules in an incomplete ordered information system, Int. J. Intell. Syst. 20 (2005) 13-27.
20. J. Stefanowski and A. Tsoukias, Incomplete information tables and rough classification, Comput. Intell. 17 (2001) 545-566.
21. J. Stefanowski and A. Tsoukias, On the extension of rough sets under incom-plete information, New Directions in Rough Sets, Data Mining and Granular-Soft Computing, 7th Int. Workshop, RSFDGrC '99, eds, N. Zhong, A. Skowron and S. Ohsuga, Yamaguchi, Japan, November 9-11, 1999, pp. 73-82.
22. R. S lowinski and D. Vanderpooten, A generalized de nition of rough approximations based on similarity, IEEE T. Knowl. Data En. 12 (2000) 331-336.
23. W. Z. Wu, W. X. Zhang and H. Z. Li, Knowledge acquisition in incomplete fuzzy information systems via the rough set approach, Expert. Syst, 20 (2003) 280-286.
24. W. Z. Wu, Attribute reduction based on evidence theory in incomplete decision systems, Inform. Sci. 178 (2008) 1355-1371.
25. X. B. Yang, J. Y. Yang, C. Wu and D. J. Yu, Dominance-based rough set approach and knowledge reductions in incomplete ordered information system, Inform. Sci. 178 (2008) 1219-1234.
26. X. B. Yang, J. Xie, X. N. Song and J. Y. Yang, Credible rules in incomplete decision system based on descriptors, Knowl.-Based Syst. 22 (2009) 8-17.
27. S. Tsumoto, Automated discovery of positive and negative knowledge in clinical databases, IEEE Eng. Med. Bio. 19 (2000) 56-62.
28. Y. Y. Yao, Constructive and algebraic methods of theory of rough sets, Inform. Sci. 109 (1998) 21-47.