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On rough approximations of groups

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Abstract

It is one of useful methods for research of group theory to construct a new group by using known groups. Lower and upper approximation operators of rough sets are applied into group theory and so the notion of rough group has been introduced. In this paper, we further study the properties of rough groups and examine relationships between subgroups and their rough approximations and relationships between a normal series of a group and its rough approximations.

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References

  1. Biswas R, Nanda S (1994) Rough groups and rough subgroups. Bull Polish Acad Sci Math 42:251–254

    MathSciNet  MATH  Google Scholar 

  2. Chen DG, Zhang WX (2002) The lower and upper approximations of fuzzy sets in a fuzzy group. Lect Notes Artif Intell 2275:502–508

    Google Scholar 

  3. Chen DG, Zhang WX, Yeung D, Tsang ECC (2006) Rough approximation on a complete completely distributive lattice with applications to generalized rough sets. Inf Sci 176:1829–1848

    Article  MathSciNet  MATH  Google Scholar 

  4. Davvaz B (2006) Roughness based on fuzzy ideals. Inf Sci 164:2417–2437

    Article  MathSciNet  Google Scholar 

  5. Davvaz B, Mahdavipour M (2006) Roughness in modules. Inf Sci 176:3658–3674

    Article  MathSciNet  MATH  Google Scholar 

  6. Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Internat J Genaral Syst 17:191–209

    Article  MATH  Google Scholar 

  7. Eric CC, Tsang D, Chen DS, Yeung X, Wang J (2008) Lee, attributes reduction using fuzzy rough sets. IEEE Trans Fuzzy Syst 16(5):1130–1141

    Article  Google Scholar 

  8. Greco S, Matarazzo B, Slowinski R (2002) Rough sets methodology for sorting problems in presence of multiple attributes and criteria. Eur J Oper Res 38:247–259

    Article  MathSciNet  Google Scholar 

  9. He Q, Wu CX (2011) Membership evaluation and feature selection for fuzzy support vector machine based on fuzzy rough sets. Soft Comput 15(6):1105–1114

    Article  MathSciNet  Google Scholar 

  10. He Q, Wu CX (2011) Separating theorem of samples in Banach space for support vector machine learning. Int J Mach Learn Cybern 2(1):49–54

    Article  Google Scholar 

  11. Hu QH, Yu DR, Xie ZX (2006) Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recognit Lett 27(5):414–423

    Article  Google Scholar 

  12. Kazanc O, Davvaz B (2008) On the structure of rough prime (primary) ideals and rough fuzzy prime (primary) ideals in commutative rings. Inf Sci 178:1343–1354

    Article  Google Scholar 

  13. Kryszkiewicz M (1999) Rules in incomplete information systems. Inf Sci 113:271–292

    Article  MathSciNet  MATH  Google Scholar 

  14. Kuroki N (1997) Rough ideals in semigroups. Inf Sci 100:139–163

    Article  MathSciNet  MATH  Google Scholar 

  15. Lin TY (1988) Neighborhood systems and relational database. In: Proceedings of 1988 ACM sixteenth annual computer science conference, 23–25 February 1988

  16. Lin TY (1989) Neighborhood systems and approximation in database and knowledge base systems. In: Proceedings of the fourth international symposium on methodologies of intelligent systems, pp 75–86

  17. Mi JS, Zhang WX (2004) An axiomatic characterization of fuzzy generalization of rough sets. Inf Sci 160:235–249

    Article  MathSciNet  MATH  Google Scholar 

  18. Mi JS, Leung Y, Zhao HY, Feng T (2008) Generalized fuzzy rough sets determined by a triangular norm. Inf Sci 178:3203–3213

    Article  MathSciNet  MATH  Google Scholar 

  19. Morsi NN, Yakout MM (1998) Axiomatics for fuzzy rough sets. Fuzzy Sets Syst 100:327–342

    Article  MathSciNet  MATH  Google Scholar 

  20. Pawlak Z (1982) Rough sets. Int J Comput Inform Sci 11:341–356

    Article  MathSciNet  MATH  Google Scholar 

  21. Pawlak Z, Skowron A (2006) Rudiments of rough sets. Inf Sci 177:3–27

    Article  MathSciNet  Google Scholar 

  22. Qian YH, Liang JY (2008) Positive approximation and rule extracting in incomplete information systems. Int J Comput Sci Knowl Eng 2(1):51–63

    Google Scholar 

  23. Radzikowska AM, Kerre EE (2002) A comparative study of fuzzy rough sets. Fuzzy Sets Syst 126:137–155

    Article  MathSciNet  MATH  Google Scholar 

  24. Slowinski R, Vanderpooten D (2000) A generalized definition of rough approximations based on similarity. IEEE Trans Data Knowl Eng 2:331–336

    Article  Google Scholar 

  25. Thiele H (2000) On axiomatic characterisations of crisp approximation operators. Inf Sci 129:221–226

    Article  MathSciNet  MATH  Google Scholar 

  26. Wang CZ, Chen DG (2010) A short note on some properties of rough groups. Comput Math Appl 59:431–436

    Article  MathSciNet  MATH  Google Scholar 

  27. Wang XZ, Tsang E, Zhao SY, Chen DG, Yeung D (2007) Learning fuzzy rules from fuzzy examples based on rough set techniques. Inf Sci 177(20):4493–4514

    Article  MathSciNet  MATH  Google Scholar 

  28. Wang CZ, Wu CX, Chen DG (2008) A systematic study on attribute reduction with rough sets based on general binary relations. Inf Sci 178:2237–2261

    Article  MathSciNet  MATH  Google Scholar 

  29. Wang XZ, Zhai JH, Lu SX (2008) Induction of multiple fuzzy decision trees based on rough set technique. Inf Sci 178(16):3188–3202

    Article  MathSciNet  MATH  Google Scholar 

  30. Wu WZ, Zhang WX (2002) Neighborhood operator systems and approximations. Inf Sci 144:201–217

    Article  MATH  Google Scholar 

  31. Wu WZ, Zhang WX (2004) Constructive and axiomatic approaches of fuzzy approximation operators. Inf Sci 159:233–254

    Article  MATH  Google Scholar 

  32. Wu WZ, Mi JS, Zhang WX (2003) Generalized fuzzy rough sets. Inf Sci 151:263–282

    Article  MathSciNet  MATH  Google Scholar 

  33. Yang X, Song X, Chen Z, Yang J (2011) On multigranulation rough sets in incomplete information system. Int J Mach Learn Cybern. doi:10.1007/s13042-011-0054-8

    Google Scholar 

  34. Yao YY (1998) Relational interpretations of neighborhood operators and rough set approximation operators. Inf Sci 111:239–259

    Article  MATH  Google Scholar 

  35. Yao YY (1998) Constructive and algebraic method of theory of rough sets. Inf Sci 109:21–47

    Article  MATH  Google Scholar 

  36. Yeung DS, Chen DG, Tsang ECC, Lee WT, Wang XZ (2005) On the generalization of fuzzy rough sets. IEEE Trans Fuzzy Syst 13:343–361

    Article  Google Scholar 

  37. Zhai JH (2011) Fuzzy decision tree based on fuzzy-rough technique. Soft Comput 15(6):1087–1096

    Article  Google Scholar 

  38. Zhu W, Wang S (2011) Matroidal approaches to generalized rough sets based on relations. Int J Mach Learn Cybern 2(4):273–279

    Article  Google Scholar 

Download references

Acknowledgments

This research is supported by Natural Science of Foundation of China (Grant No. 61070242). This research is also supported by the natural science foundation of Hebei Province (F2012201023), Shenzhen Key Laboratory for High Performance Data Mining with Shenzhen New Industry Development Fund under grant No. CXB201005250021A, and Scientific Research Project of Hebei University (09265631D-2).

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Correspondence to Changzhong Wang.

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Wang, C., Chen, D. & Hu, Q. On rough approximations of groups. Int. J. Mach. Learn. & Cyber. 4, 445–449 (2013). https://doi.org/10.1007/s13042-012-0108-6

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