Skip to main content

Restructuring of Rough Sets for Fuzzy Random Data of Creative City Evaluation

  • Chapter
Integrated Uncertainty Management and Applications

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 68))

  • 984 Accesses

Abstract

In this paper we provide the restructuring method of rough sets for analyzing fuzzy random data that many experts evaluate creative cities. Usually it is hard to clarify the situation where randomness and fuzziness exist simultaneously. This paper presents a method based on fuzzy random variables to restructure a rough set. The algorithms of rough set is used to distinguish whether a subset can be classified in the object set or not based on confidence interval. The expected-value-approach is also applied to calculate the fuzzy value with probability into a scalar value.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Florida, R.: The rise of the creative class. Basic Books, New York (2004)

    Google Scholar 

  2. Geng, Z.Q., Zhu, Q.X.: Rough set-based heuristic hybrid recognizer and its application in fault diagnosis. Expert Systems with Applications 36(2), part 2, 2711–2718 (2009)

    Google Scholar 

  3. Gil, M.A., Miguel, L.D., Ralescu, D.A.: Overview on the development of fuzzy random variables. Fuzzy sets and systems 157(19), 2546–2557 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  4. Howkins, J.: The Creative Economy: How People Make Money from Ideas. Penguin Global (2002)

    Google Scholar 

  5. Kruse, R., Meyer, K.D.: Statistics with Vague Data. Reidel Publishing Company, Dordrecht (1987)

    MATH  Google Scholar 

  6. Kwakernaak, H.: Fuzzy random variables–I. Definitions and theorems. Information Sciences 15(1), 1–29 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  7. Kwakernaak, H.: Fuzzy random variables–II. Algorithm and examples. Information Sciences 17(3), 253–278 (1979)

    Article  MATH  MathSciNet  Google Scholar 

  8. Landry, C.: The Creative City: A Toolkit for Urban Innovators. Earth Scan Publications, London (2000)

    Google Scholar 

  9. Lin, L., Zhu, J., Watada, J.: A rough set approach to classification and its application for the creative city development. Journal of Innovative Computing, Information and Control 5(12), 4859–4866 (2009)

    Google Scholar 

  10. Lin, T.Y., Cercone, N. (eds.): Rough Sets and Data Mining: Analysis of Imprecise Data. Kluwer Academic, Dordrecht (1997)

    MATH  Google Scholar 

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

    MATH  Google Scholar 

  12. Liu, B.: Uncertainty Theory, 2nd edn. Springer, Berlin (2007)

    MATH  Google Scholar 

  13. Liu, B., Liu, Y.K.: Expected value of fuzzy variable and fuzzy expected value models. IEEE Transaction on Fuzzy Systems 10(4), 445–450 (2002)

    Article  Google Scholar 

  14. Liu, Y.-K., Liu, B.: Fuzzy random variable: A scalar expected value operator. Fuzzy Optimization and Decision Making 2(2), 143–160 (2003)

    Article  MathSciNet  Google Scholar 

  15. Liu, Y.K., Liu, B.: Fuzzy random programming with equilibrium chance constraints. Infromation Sicinece 170(25), 363–395 (2005)

    Article  MATH  Google Scholar 

  16. Liu, Y.K., Liu, B.: On minimum-risk problems in fuzzy random decision systems. Computers & Operations Research 32(2), 257–283 (2005)

    MATH  MathSciNet  Google Scholar 

  17. López-Diaz, M., Gil, M.A.: Constructive definitions of fuzzy random variables. Statistics and Probability Letters 36(2), 135–143 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  18. Nahmias, s.: Fuzzy variables. Fuzzy Sets and Systems 1(2), 97–111 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  19. Negoita, C.V., Ralescu, D.A.: Application of Fuzzy Sets to Systems Analsyis. Birkhauser Verlag, Basel

    Google Scholar 

  20. Nguyen, H.T.: A note on the extension principle for fuzzy sets. Journal of Mathematical Analysis and Applications 64(2), 369–380 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  21. Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11(5), 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  22. Puri, M.L., Ralescu, D.A.: Fuzzy random variables. Journal of Mathematical Analysis and Applications 114(2), 409–422 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  23. Tanaka, H., Watada, J.: Possibilistic linear systems and their application to the linear regression model. Fuzzy Sets and Systems 27(3), 275–289 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  24. Wang, G.Y., Qiao, Z.: Linear programming with fuzzy random variable coefficients. Fuzzy sets and Systems 57(3), 295–311 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  25. Wang, S., Watada, J.: Studying distribution functions of fuzzy random variables and its applications to critical value functions. International Journal of Innovative Computing, Information & Control 5(2), 279–292 (2009)

    Google Scholar 

  26. Watada, J., Wang, S.: Regression model based on fuzzy random variables. In: Seising, R. (ed.) Views on Fuzzy Sets and Systems from Different Perspectives, ch. 26, Spring-Verlag, Berlin (2009)

    Google Scholar 

  27. Watada, J., Wang, S., Pedrycz, W.: Building confidence-interval-based fuzzy random regression models. IEEE Transactions on Fuzzy Systems 17(6) (2009) (in press)

    Google Scholar 

  28. Wikipedia. Rough Set (2008), http://en.wikipedia.org/wiki/Rough_set (Cited December 10, 2008)

  29. Yao, J.T., Yao, Y.Y.: Induction of classification rules by granular computing. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 331–338. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  30. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning-I. Information Science 8(3), 199–249, 8(4), 301–357, 9(1), 43–80 (1975)

    Article  MathSciNet  Google Scholar 

  31. Zhuang, Z.Y., Churilov, L., Burstein, F., Sikaris, K.: Combining data mining and case-based reasoning for intelligent decision support for pathology ordering by general practitioners. European Journal of Operational Research 195(3), 662–675 (2009)

    Article  MATH  Google Scholar 

  32. Ziarko, W.: Rough sets as a methodology for data mining. In: Rough Sets in Knowledge Disco 1: Methodology and Applications, pp. 554–576. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Lin, LC., Watada, J. (2010). Restructuring of Rough Sets for Fuzzy Random Data of Creative City Evaluation. In: Huynh, VN., Nakamori, Y., Lawry, J., Inuiguchi, M. (eds) Integrated Uncertainty Management and Applications. Advances in Intelligent and Soft Computing, vol 68. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11960-6_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-11960-6_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11959-0

  • Online ISBN: 978-3-642-11960-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics