Data analysis based on discernibility and indiscernibility

Information Sciences

Volumn 177, Issue 22, 2007, Pages 4959-4976

  Zhao, Yan ^a   Yao, Yiyu ^a   Luo, Feng ^a  

^a UNIVERSITY OF REGINA   (Canada)

Author keywords

Indiscernibility and discernibility matrices; Indiscernibility and discernibility relations; Reducts; Rough sets

Indexed keywords

DATA REDUCTION; DATA STRUCTURES; INFORMATION SCIENCE; MATRIX ALGEBRA;

DISCERNIBILITY MATRICES; DISCERNIBILITY RELATIONS;

ROUGH SET THEORY;

EID: 34548258153     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2007.06.031     Document Type: Article

References (38)

1. G Bazan J., Nguyen H.S., Nguyen S.H., Synak P., and Wróblewski J. Rough set algorithms in classification problems. In: Polkowski L., Tsumoto S., and Lin T.Y. (Eds). Rough Set Methods and Applications (2000), Physica Verlag, Heidelberg 49-88
2. Chen D., Wang C., and Hu Q. A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets. Information Sciences 177 (2007) 3500-3518
3. Demri S., and Orlowska E. Logical analysis of indiscernibility. In: Orlowska E. (Ed). Incomplete Information: Rough Set Analysis (1998), Physica-Verlag, Heidelberg 347-380
4. Grzymala-Busse J.W. LERS: a system for learning from examples based on rough sets. In: Słowiński R. (Ed). Intelligent Decision Support: Handbook of Advances and Applications of the Rough Sets Theory (1992), Kluwer, Dordrecht 3-18
5. Grzymala-Busse J.W. Characteristic relations for incomplete data: a generalization of the indiscernibility Relation. Transactions of Rough Sets (2005) 58-68
6. M. Hadjumichael, S.K.M. Wong, Fuzzy representation in rough set approximations. International Workshop on Rough Sets and Knowledge Discovery in Databases, 1993, pp. 349-356.
7. M.M. Kokar, On similarity methods in machine learning, in: Proceedings of the International Workshop on Similarity Methods, 2000, pp. 15-25.
8. Mi J.S., Wu W.Z., and Zhang W.X. Approaches to knowledge reduction based on variable precision rough set model. Information Sciences 159 (2004) 255-272
9. Nguyen S.H. Regularity analysis and its applications in data mining. In: Polkowski L., Tsumoto S., and Lin T.Y. (Eds). Rough Set Methods and Applications (2000), Physica Verlag, Heidelberg 289-378
10. Novotný M., and Pawlak Z. On a representation of rough sets by means of information systems. Fundamenta Informaticae 6 (1983) 286-296
11. Orlowska E. Semantics analysis of inductive reasoning. Theoretical Computer Science 43 (1986) 81-89
12. Orlowska E. Introduction: what you always wanted to know about rough sets. In: Orlowska E. (Ed). Incomplete Information: Rough Set Analysis (1998), Physica Verlag, Heidelberg, New York 1-20
13. Pawlak Z. Rough Sets: Theoretical Aspects of Reasoning About Data (1991), Kluwer Academic Publishers, Dordrecht, MA
14. Z. Pawlak, Granularity of knowledge, indiscernibility and rough sets, in: Proceedings of the IEEE International Conference on Fuzzy Systems, 1998, pp. 106-110.
15. Z. Pawlak, Conflicts and negotiations, in: Proceedings of Rough Sets and Knowledge Technology, 2006, pp. 12-27.
16. L. Polkowski, A. Skowron, Rough mereology, in: Proceedings of ISMIS, 1994, pp. 85-94.
17. Polkowski L., Skowron A., and Zytkow J. Tolerance based rough sets. In: Lin T.Y., and Wildberger A.M. (Eds). Soft Computing: Rough Sets, Fuzzy Logic, Neural Networks, Uncertainty Management (1995), Simulation Councils, Inc., San Diego 55-58
18. G.F. Qiu, W.X. Zhang, W.Z. Wu, Characterizations of attributes in generalized approximation representation spaces, in: Proceedings of the 10th International Conference on RSFDGrC, 2005, pp. 84-93.
19. Quafafou M. α-RST: a generalization of rough set theory. Information Sciences 124 (2000) 301-316
20. Skowron A., and Polkowski L. Rough mereological foundations for design, analysis, synthesis, and control in distributed systems. Information Sciences 104 (1998) 129-156
21. Skowron A., and Rauszer C. The discernibility matrices and functions in information systems. In: Slowińi R. (Ed). Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory (1992), Kluwer Academic Publishers, Dordrecht
22. A. Skowron, J. Stepaniuk, Decision rules based on discernibility matrices and decision matrices, in: Proceedings of the International Workshop on Rough Sets and Soft Computing (RSSC'94), 1994, pp. 156-163.
23. Slezak D. Various approaches to reasoning with frequency based decision reducts: a surve. In: Polkowski L., Tsumoto S., and Lin T.Y. (Eds). Rough Set Methods and Applications (2000), Physica-verlag, Heidelberg 235-285
24. Slowinski R., and Vanderpooten D. A generalized definition of rough approximation based on similarity. IEEE Transaction on Data and Knowledge Engineering 12 2 (2000) 331-336
25. R. Slowinski, J. Stefanowski, Handling various types of uncertainty in the rough set approach, in: Proceedings of the International Workshop on Rough Sets and Knowledge Discovery, pp. 366-376.
26. J. Stefanowski, A. Tsoukias, Valued tolerance and decision rules, in: Proceedings of Rough Sets and Current Trends in Computing, 2000, pp. 170-181.
27. R. Susmaga, Tree-like parallelization of reduct and construct computation, in: Proceedings of Rough Sets and Current Trends in Computing, 2004, pp. 455-464.
28. Susmaga R. Reducts and constructs in attribute reduction. Fundamenta Informaticae 61 2 (2004) 159-181
29. Swiniarski R.W. Rough sets methods in feature reduction and classification. International Journal of Applied Mathematics and Computer Science 11 3 (2001) 565-582
30. Wang G.Y. Rough reduction in algebra view and information view. International Journal of Intelligent Systems 18 6 (2003) 679-688
31. Wang J., and Wang J. Reduction algorithms based on discernibility matrix: the ordered attributes method. Journal of Computer Science and Technology 16 (2001) 489-504
32. Wong S.K.M., and Ziarko W. On optimal decision rules in decision tables. Bulletin of the Polish Academy of Sciences and Mathematics (1985) 693-696
33. Yao Y.Y. Relational interpretations of neighborhood operators and rough set approximation operators. Information Sciences 111 1-4 (1998) 239-259
34. Yao Y.Y., and Lin T.Y. Generalization of rough sets using modal logic. Intelligent Automation and Soft Computing 2 (1996) 103-120
35. Y.Y. Yao, S.K.M. Wong, Generalization of rough sets using relationships between attribute values, in: Proceedings of the Joint Conference of Information Sciences, 1995, pp. 30-33.
36. Zhao K., and Wang J. A reduction algorithm meeting users' requirements. Journal of Computer Science and Technology 17 (2002) 578-593
37. Zhu W., and Wang F.Y. Reduction and axiomization of covering generalized rough sets. Information Sciences 152 (2003) 217-230
38. Ziarko W. Rough set approaches for discovering rules and attribute dependencies. In: Klösgen W., and Żytkow J.M. (Eds). Handbook of Data Mining and Knowledge Discovery (2000), Oxford 328-339