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Knowledge Granulation in Interval-Valued Information Systems Based on Maximal Consistent Blocks

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Rough Sets and Knowledge Technology (RSKT 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8818))

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Abstract

Rough set theory, proposed by Pawlak in the early 1980s, is an extension of the classical set theory for modeling uncertainty or imprecision information. In this paper, we investigate partial relations and propose the concept of knowledge granulation based on the maximal consistent block in interval-valued information systems. The knowledge granulation can provide important approaches to measuring the discernibility of different knowledge in interval-valued information systems. These results in this paper may be helpful for understanding the essence of rough approximation and attribute reduction in interval-valued information systems.

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References

  1. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  2. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Boston (1991)

    Book  MATH  Google Scholar 

  3. Yao, Y.Y.: On Generalizing Pawlak Approximation Operators. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 298–307. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  4. Yao, Y.Y., Miao, D.Q., Xu, F.F.: Granular Structures and Approximations in Rough Sets and Knowledge Spaces. In: Ajith, A., Rafael, F., Rafael, B. (eds.) Rough Set Theory: A True Landmark in Data Analysis. Springer, Berlin (2009)

    Google Scholar 

  5. Yao, Y.Y.: Probabilistic approaches to rough sets. Expert Systems 20, 287–297 (2003)

    Article  Google Scholar 

  6. Qian, Y.H., Liang, J.Y., Yao, Y.Y., et al.: MGRS: A multi-granulation rough set. Information Sciences 6, 949–970 (2010)

    Article  MathSciNet  Google Scholar 

  7. Wang, G.Y., Zhang, Q.H., Ma, X.A., Yang, Q.S.: Granular computing models for knowledge uncertainty. Chinese Journal of Software 4, 676–694 (2011)

    Article  Google Scholar 

  8. Qian, Y.H., Liang, J.Y., Dang, C.Y.: Incomplete multi-granulation rough set. IEEE Transactions on Systems, Man, and Cybernetics: Part A 2, 420–431 (2012)

    Google Scholar 

  9. Miao, D.Q., Zhao, Y., Yao, Y.Y., et al.: Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model. Information Sciences 24, 4140–4150 (2009)

    Article  MathSciNet  Google Scholar 

  10. Shannon, C.E.: The mathematical theory of communication. The Bell System Technical Journal 3-4, 373–423 (1948)

    MathSciNet  Google Scholar 

  11. Beaubouef, T., Petry, F.E., Arora, G.: Information-theoretic measures of uncertainty for rough sets and rough relational databases. Information Sciences 109, 185–195 (1998)

    Article  Google Scholar 

  12. Miao, D.Q., Fan, S.D.: The calculation of knowledge granulation and its application. Journal of Systems Engineering: Theory and Practice 24, 93–96 (2002)

    Google Scholar 

  13. Liang, J.Y., Shi, Z.Z.: The information entropy, rough entropy and knowledge granularity in rough set theory. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 1, 37–46 (2004)

    Article  MathSciNet  Google Scholar 

  14. Liang, J.Y., Shi, Z.Z., Li, D., Wierman, M.: Information entropy, rough entropy and knowledge granularity in incomplete information systems. International Journal of General Systems 35, 641–654 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  15. Qian, Y., Liang, J.: Combination entropy and combination granulation in incomplete information system. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 184–190. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  16. Xu, W.H., Zhang, X.Y., Zhang, W.X.: Knowledge granulation, knowledge entropy and knowledge uncertainty measure in ordered information systems. Applied Soft Computing 9, 1244–1251 (2009)

    Article  Google Scholar 

  17. Yao, Y.Y., Zhao, L.Q.: A measurement theory view on the granularity of partitions. Information Sciences 213, 1–13 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  18. Zhang, N., Miao, D.Q., Yue, X.D.: Knowledge reduction in interval-valued information systems. Journal of Computer Research and Development 47, 1362–1371 (2010)

    Google Scholar 

  19. Zhang, N.: Research on Interval-valued Information Systems and Knowledge Spaces: A Granular Approach. PhD Thesis, Tongji University, China (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer and Control Engineering, Yantai University, Yantai, Shandong, 264005, China

    Nan Zhang

  2. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Xiaodong Yue

Authors
  1. Nan Zhang
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  2. Xiaodong Yue
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Corresponding author

Correspondence to Nan Zhang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    Duoqian Miao

  2. Department of Electrical and Computer En, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  3. University of Warsaw, Warsaw, Poland

    Dominik Ślȩzak

  4. University of Applied Sciences, München, Germany

    Georg Peters

  5. Tianjin University, Tianjin, China

    Qinghua Hu

  6. Tongji University, Shanghai, China

    Ruizhi Wang

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© 2014 Springer International Publishing Switzerland

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Zhang, N., Yue, X. (2014). Knowledge Granulation in Interval-Valued Information Systems Based on Maximal Consistent Blocks. In: Miao, D., Pedrycz, W., Ślȩzak, D., Peters, G., Hu, Q., Wang, R. (eds) Rough Sets and Knowledge Technology. RSKT 2014. Lecture Notes in Computer Science(), vol 8818. Springer, Cham. https://doi.org/10.1007/978-3-319-11740-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-11740-9_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11739-3

  • Online ISBN: 978-3-319-11740-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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