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Rough Set Approximations Based on Granular Labels

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Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5908))

Abstract

In this paper, rough set approximations based on labelled blocks are explored. The concept of labelled blocks determined by a function is first introduced. Lower and upper label-block approximations of sets are then defined. Properties of label-block approximation operators are also examined. Finally, relationship between properties of label-block approximation operators and some essential properties of the corresponding function is characterized.

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References

  1. Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Boston (2002)

    Google Scholar 

  2. Bargiela, A., Pedrycz, W.: Toward a theory of granular computing for human-centered information processing. IEEE Transactions on Fuzzy Systems 16, 320–330 (2008)

    Article  Google Scholar 

  3. Bittner, T., Smith, B.: A theory of granular partitions. In: Duckham, M., Goodchild, M.F., Worboys, M.F. (eds.) Foundations of Geographic Information Science, pp. 117–151. Taylor & Francis, Abington (2003)

    Google Scholar 

  4. Bittner, T., Stell, J.: Stratified rough sets and vagueness. In: Kuhn, W., Worboys, M.F., Timpf, S. (eds.) COSIT 2003. LNCS, vol. 2825, pp. 270–286. Springer, Heidelberg (2003)

    Google Scholar 

  5. Inuiguchi, M., Hirano, S., Tsumoto, S. (eds.): Rough Set Theory and Granular Computing. Springer, Berlin (2002)

    Google Scholar 

  6. Leung, Y., Li, D.: Maximal consistent block technique for rule acquisition in incomplete information systems. Information Sciences 153, 85–106 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  7. Leung, Y., Wu, W.-Z., Zhang, W.-X.: Knowledge acquisition in incomplete information systems: A rough set approach. European Journal of Operational Research 168, 164–180 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  8. Lin, T.Y.: Granular computing: From rough sets and neighborhood systems to information granulation and computing with words. In: European Congress on Intelligent Techniques and Soft Computing, September 8-12, pp. 602–1606 (1997)

    Google Scholar 

  9. Lin, T.Y., Yao, Y.Y., Zadeh, L.A. (eds.): Data Mining, Rough Sets and Granular Computing. Physica-Verlag, Heidelberg (2002)

    MATH  Google Scholar 

  10. Ma, J.-M., Zhang, W.-X., Leung, Y., Song, X.-X.: Granular computing and dual Galois connection. Information Sciences 177, 5365–5377 (2007)

    Article  MATH  MathSciNet  Google Scholar 

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

    MATH  Google Scholar 

  12. Pedrycz, W. (ed.): Granular Computing: An Emerging Paradigm. Physica-Verlag, Heidelberg (2001)

    MATH  Google Scholar 

  13. Pedrycz, W., Skowron, A., Kreinovich, V. (eds.): Handbook of Granular Computing. Wiley-Interscience, Hoboken (2008)

    Google Scholar 

  14. Qian, Y.H., Liang, J.Y., Dang, C.Y.: Knowledge structure, knowledge granulation and knowledge distance in a knowledge base. International Journal of Approximate Reasoning 50, 174–188 (2009)

    Article  MathSciNet  Google Scholar 

  15. Skowron, A., Stepaniuk, J.: Information granules: Towards foundations of granular computing. International Journal of Intelligent Systems 16, 57–85 (2001)

    Article  MATH  Google Scholar 

  16. Wu, W.-Z., Leung, Y., Mi, J.-S.: Granular computing and knowledge reduction in formal contexts. IEEE Transactions on Knowledge and Data Engineering 21, 1461–1474 (2009)

    Article  Google Scholar 

  17. Yager, R.R.: Intelligent social network analysis using granular computing. International Journal of Intelligent Systems 23, 1196–1219 (2008)

    Google Scholar 

  18. Yao, J.T.: Recent developments in granular computing: A bibliometrics study. In: Proceedings of IEEE International Conference on Granular Computing, Hangzhou, China, August 26–28, pp. 74–79 (2008)

    Google Scholar 

  19. Yao, Y.Y.: Information granulation and rough set approximation. International Journal of Intelligent Systems 16, 87–104 (2001)

    Article  MATH  Google Scholar 

  20. Yao, Y.Y.: A partition model of granular computing. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 232–253. Springer, Heidelberg (2004)

    Google Scholar 

  21. Yao, Y.Y., Liau, C.-J., Zhong, N.: Granular computing based on rough sets, quotient space theory, and belief functions. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 152–159. Springer, Heidelberg (2003)

    Google Scholar 

  22. Zadeh, L.A.: Fuzzy sets and information granularity. In: Gupta, N., Ragade, R., Yager, R.R. (eds.) Advances in Fuzzy Set Theory and Applications, pp. 3–18. North-Holland, Amsterdam (1979)

    Google Scholar 

  23. Zadeh, L.A.: Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems 90, 111–127 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  24. Zhang, W.-X., Leung, Y., Wu, W.-Z.: Information Systems and Knowledge Discovery (in Chinese). Science Press, Beijing (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Mathematics, Physics and Information Science, Zhejiang Ocean University, Zhoushan, Zhejiang, 316004, P.R. China

    Wei-Zhi Wu

Authors
  1. Wei-Zhi Wu
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Aided Sciences, Kyushu Institute of Technology, 804-8550, Tobata, Kitakyushu, Japan

    Hiroshi Sakai

  2. Department of Pure Mathematics, University of Calcutta, 35 Ballygunge Circular Road, 700019, Kolkata, India

    Mihir Kumar Chakraborty

  3. Information Technology Department, University of Cairo, 5 Ahmed Zewal St. Orman, Giza, Egypt

    Aboul Ella Hassanien

  4. University of Warsaw & Infobright Inc., Poland

    Dominik Ślęzak

  5. University of Electronic Science and Technology of China, Chengdu, China

    William Zhu

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© 2009 Springer-Verlag Berlin Heidelberg

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Wu, WZ. (2009). Rough Set Approximations Based on Granular Labels. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_11

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  • DOI: https://doi.org/10.1007/978-3-642-10646-0_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10645-3

  • Online ISBN: 978-3-642-10646-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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