A New Similarity Measure Between Vague Sets

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 367)

Abstract

Similarity measure is one of important, effective and widely-used methods in data processing and analysis. Vague set, as a generalized fuzzy set, has more powerful ability to process fuzzy information than fuzzy set. In this paper, we propose a new similarity measure between vague sets. Compared to existing similarity measures, our approach is far more reasonable, practical yet useful in measuring the similarity between vague sets.

Keywords

Fuzzy sets Vague sets Similarity measure 

Notes

Acknowledgments

The research was supported by the National Natural Science Foundation of China (Grant No. 60972115) and the Scientific Research Common Program of Beijing Municipal Commission of Education (Grant No. SQKM201211232016).

References

  1. 1.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)CrossRefMathSciNetMATHGoogle Scholar
  2. 2.
    Gau, W.L., Buehrer, D.J.: Vague sets. IEEE Trans. Syst. Man. Cybern. 23(2), 610–614 (1993)CrossRefMATHGoogle Scholar
  3. 3.
    Chen, S.M.: Measures of similarity between vague sets. Fuzzy Sets Syst. 74(2), 217–223 (1995)CrossRefMATHGoogle Scholar
  4. 4.
    Hong, D.H., Kim, C.: A note on similarity measures between vague sets and elements. Inf. Sci. 115, 83–96 (1999)CrossRefMathSciNetMATHGoogle Scholar
  5. 5.
    Li, F., Xu, Z.Y.: Similarity measures between vague sets. Chinese J. Software 12(6), 922–927 (2001)Google Scholar
  6. 6.
    Li, Y.H., Chi, Z.X., Yan, D.Q.: Vague similarity and vague entropy. Comput. Sci. 29(12), 129–132 (2002). (Chinese journal)Google Scholar
  7. 7.
    Szmidt, E., Kacprzyk, J.: Distances between intuitionistic fuzzy sets. Fuzzy Sets Syst. 114, 505–518 (2000)CrossRefMathSciNetMATHGoogle Scholar
  8. 8.
    Grzegorzewski, P.: Distances Between Intuitionistic Fuzzy Sets and/or Interval valued Fuzzy Sets Based on the Hausdorff Metric. Fuzzy Sets and Systems, 2003Google Scholar
  9. 9.
    Pappis, C.P., Karacapilidis, N.I.: A comparative assessment of measures of similarity of fuzzy values. Fuzzy Sets Syst. 56, 171–174 (1993)CrossRefMathSciNetMATHGoogle Scholar
  10. 10.
    Zhang, W.N., Wang, K.: An efficient evaluation of a fuzzy equi-join using fuzzy equality indicators. IEEE Trans. Knowl. Data Eng. 12(2), 225–237 (2000)CrossRefGoogle Scholar
  11. 11.
    Ma, Z.M., Zhang, W.J., Ma, W.Y.: Semantic measure of fuzzy data in extended possibility-based fuzzy relational database. Int. J. Intell. Syst. 15(8), 705–716 (2000)CrossRefMATHGoogle Scholar
  12. 12.
    Lu, A., Ng, W.: Managing merged data by vague functional dependencies. Lect. Notes Comput. Sci. 3288, 259–272 (2004)CrossRefGoogle Scholar
  13. 13.
    Li, Y.H., Olson, D.L., Qin, Z.: Similarity measures between intuitionistic fuzzy (vague) sets: A comparative analysis. Pattern Recogn. Lett. 28(2), 278–285 (2007)Google Scholar
  14. 14.
    Zhang, D.F., Zhang, J.L., Lai, K.K., Lu, Y.: An Novel approach to supplier selection based on vague sets group decision. Expert Syst. Appl. 36(5), 9557–9563 (2009)CrossRefGoogle Scholar
  15. 15.
    Zhang, Q.S., Jiang, S.Y.: A note on information entropy measures for vague sets and its applications. Inf. Sci. 178, 4184–4191 (2008)CrossRefMATHGoogle Scholar
  16. 16.
    Ye, J.: Using an improved measure function of vague sets for multicriteria fuzzy decision-making. Expert Syst. Appl. 37, 4706–4709 (2010)CrossRefGoogle Scholar
  17. 17.
    Geng, X.L, Chu, X.N., Zhang, Z.F.: Test article sample title placed here. Expert Syst. Appl. 37, 6629–6638 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Applied ScienceBeijing Information Science and Technology UniversityBeijingChina

Personalised recommendations