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Dependency space, closure system and rough set theory

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Abstract

This paper researches on potential relations of dependency space, closure system and rough set theory, and mainly focuses on solving some essential problems of rough set theory based on dependency space and closure system respectively. Firstly, we pretreat an information system into a relatively simple derivative system, in which dependency space and closure system are generated; Secondly, by means of dependency space and closure system separately we can solve some essential problems of rough set theory, such as reducts, cores; Finally, we reveal interior relations between dependency space and closure system. Conclusions of this paper not only help to understand rough set theory from the prospective of the dependency space and closure system, but also provide a new theoretical basis for data analysis and processing.

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References

  1. Chen CB, Wang LY (2006) Rough set-based clustering with refinement using shannon’s entropy theory. Comput Math Appl 52(10–11):1563–1576

    Article  MathSciNet  MATH  Google Scholar 

  2. Cheng Y, Miao DQ, Feng QR (2011) Positive approximation and converse approximation in interval-valued fuzzy rough sets. Inf Sci 181(11):2086–2110

    Article  MathSciNet  MATH  Google Scholar 

  3. Ganter B, Wille R (1999) Formal concept analysis: mathematical foundations. Springer, Berlin

    Book  MATH  Google Scholar 

  4. 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 

  5. Hu QH, Liu JF, Yu DR (2008) Mixed feature selection based on granulation and approximation. Knowl Based Syst 21:294-304

    Article  Google Scholar 

  6. Järvinen J (1997) Representation of information systems and dependence spaces, and some basic algorithms. Licentiate’s thesis. Ph.D. thesis, University of Turku, Department of Mathematics, Turku

  7. Jensen R, Shen Q (2007) Fuzzy-rough sets assisted attribute selection. IEEE Trans Fuzzy Syst 15(1):73–89

    Article  Google Scholar 

  8. Liang JY, Dang CY, Chin KS, Yam Richard CM (2002) A new method for measuring uncertainty and fuzziness in rough set theory. Int J General Syst 31(4):331–342

    Article  MATH  Google Scholar 

  9. Mi JS, Leung Y, Wu WZ (2005) An uncertainty measure in partition-based fuzzy rough sets. Int J General Syst 34:77–90

    Article  MathSciNet  MATH  Google Scholar 

  10. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341–356

    Article  MathSciNet  MATH  Google Scholar 

  11. Pawlak Z (1998) Rough set theory and its application to data analysis. Cybern Syst 29:661–668

    Article  MATH  Google Scholar 

  12. Polkowski L, Lin TY, Tsumoto S (eds) (2000) Rough sets methods and application: new developments in knowledge discovery in information system. Physica-Verlag, Heidelberg

  13. Polkowski L, Skowron A (eds) (1998) Rough sets in knowledge discovery 1: methodology and applications. Physica-Verlag, Heidelberg

  14. Polkowski L, Skowron A (eds) (1998) Rough sets in knowledge discovery 2: applications, case studies and software systems. Physica-Verlag, Heidelberg

  15. Qian YH, Liang JY, Pedrycz W, Dang CY (2011) An efficient accelerator for attribute reduction from incomplete data in rough set framework. Pattern Recognit 44(8):1658–1670

    Article  MATH  Google Scholar 

  16. Qian YH, Liang JY, Wu WZ, Dang CY (2011) Information granularity in fuzzy binary GrC model. IEEE Trans Fuzzy Syst 19(2):253–264

    Article  Google Scholar 

  17. Qu KS, Zhai YH, Liang JY (2007) Representation and extension of rough set based on FCA. J Softw 18(9): 2174–2182

    Article  MATH  Google Scholar 

  18. Shi ZH, Gong ZT (2010) The further investigation of covering-based rough sets: uncertainty characterization, similarity measure and generalized models. Inf Sci 180(19):3745-3763

    Article  MathSciNet  MATH  Google Scholar 

  19. Skowron A (1990) The rough sets theory and evidence theory. Fundam Inform XIII:245–262

    MathSciNet  Google Scholar 

  20. 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 

  21. Yang XB, Xie J, Song XN, Yang JY (2009) Credible rules in incomplete decision system based on descriptors. Knowl Based Syst 22(1):8–17

    Article  Google Scholar 

  22. Yang XB, Song XN, Chen ZH, Yang JY On multigranulation rough sets in incomplete information system. Int J Mach Learn Cybern. doi:10.1007/s13042-011-0054-8

  23. Yao YY (1998) A comparative study of fuzzy sets and rough sets. Inf Sci 109(1–4):227–242

    Article  MATH  Google Scholar 

  24. Yao YY, Lingras PT (1998) Interpretations of belief functions in the theory of rough sets. Inf Sci 104(1–2):81–106

    Article  MathSciNet  MATH  Google Scholar 

  25. Zhang XY, Mo ZW, Xiong F, Cheng W (2012) Comparative study of variable precision rough set model and graded rough set model. Int J Approx Reason 53(1):104–116

    Article  MathSciNet  MATH  Google Scholar 

  26. Zhu W, Wang SP (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

The work is supported by the National Natural Science Foundation of China (60970014, 61070100, 61175067, 61005053 and 60875040), the Natural Science Foundation of Shanxi, China (2010011021-1), the Foundation of Doctoral Program Research of Ministry of Education of China (200801080006), Shanxi Foundation of Tackling Key Problem in Science and Technology (20110321027-02) and the Graduate Innovation Project of Shanxi Province, China (20103004).

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Correspondence to Deyu Li.

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Kang, X., Li, D. Dependency space, closure system and rough set theory. Int. J. Mach. Learn. & Cyber. 4, 595–599 (2013). https://doi.org/10.1007/s13042-012-0106-8

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  • DOI: https://doi.org/10.1007/s13042-012-0106-8

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