Skip to main content
Log in

Distance measures between type-2 intuitionistic fuzzy sets and their application to multicriteria decision-making process

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Type-2 fuzzy set (T2FS) is a generalization of the ordinary fuzzy set in which the membership value for each member of the set is itself a fuzzy set. However, it is difficult, in some situations, for the decision-makers to give their preferences towards the object in terms of single or exact number. For handling this, a concept of type-2 intuitionistic fuzzy set (T2IFS) has been proposed and hence under this environment, a family of distance measures based on Hamming, Euclidean and Hausdorff metrics are presented. Some of its desirable properties have also been investigated in details. Finally, based on these measures, a group decision making method has been presented for ranking the alternatives. The proposed measures has been illustrated with a numerical example.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Attanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20:87–96

    Article  MathSciNet  Google Scholar 

  2. Burillo P, Bustince H (1996) Entropy on intuitionistic fuzzy sets and on interval-valued fuzzy sets. Fuzzy Sets Syst 78(3):305–316

    Article  MathSciNet  MATH  Google Scholar 

  3. Chen SM, Lee LW (2010a) Fuzzy multiple attributes group decision-making based on the interval type-2 topsis method. Expert Syst Appl 37(4):2790–2798

  4. Chen SM, Lee LW (2010b) Fuzzy multiple attributes group decision-making based on the ranking values and the arithmetic operations of interval type - 2 fuzzy sets. Expert Syst Appl 37(1):824 – 833

  5. Chen SM, Yang MW, Lee LW, Yang SW (2012) Fuzzy multiple attributes group decision-making based on ranking interval type-2 fuzzy sets. Expert Syst Appl 39(5):5295–5308

    Article  Google Scholar 

  6. Chen TY (2013) A linear assignment method for multiple-criteria decision analysis with interval type-2 fuzzy sets. Appl Soft Comput 13(5):2735–2748

    Article  Google Scholar 

  7. Chen TY, Chang CH, Lu J f R (2013) The extended qualiflex method for multiple criteria decision analysis based on interval type-2 fuzzy sets and applications to medical decision making. Eur J Oper Res 226 (3):615–625

    Article  MathSciNet  MATH  Google Scholar 

  8. Garg H (2016a) Generalized intuitionistic fuzzy interactive geometric interaction operators using einstein t-norm and t-conorm and their application to decision making. Comput Ind Eng 101:53–69

  9. Garg H (2016b) Generalized pythagorean fuzzy geometric aggregation operators using einstein t-norm and t-conorm for multicriteria decision-making process. Int J Intell Syst. doi:10.1002/int.21860

  10. Garg H (2016c) A new generalized improved score function of interval-valued intuitionistic fuzzy sets and applications in expert systems. Appl Soft Comput 38:988–999

  11. Garg H (2016d) A new generalized pythagorean fuzzy information aggregation using einstein operations and its application to decision making. Int J Intell Syst 31(9):886–920

  12. Garg H (2016e) A novel accuracy function under interval-valued pythagorean fuzzy environment for solving multicriteria decision making problem. J Intell Fuzzy Syst 31(1):529–540

  13. Garg H (2016f) A novel correlation coefficients between pythagorean fuzzy sets and its applications to decision-making processes. Int J Intell Syst 31(12):1234–1253

  14. Garg H, Agarwal N, Tripathi A (2015) Entropy based multi-criteria decision making method under fuzzy environment and unknown attribute weights. Global Journal of Technology and Optimization 6:13–20

    Google Scholar 

  15. Hu J, Zhang Y, Chen X, Liu Y (2013) Multi-criteria decision making method based on possibility degree of interval type-2 fuzzy number. Knowl-Based Syst 43:21–29

    Article  Google Scholar 

  16. Hung WL, Yang MS (2004a) Similarity measures between type-2 fuzzy sets. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12(06):827–841

  17. Hung WL, Yang MS (2004b) Similarity measures of intuitionistic fuzzy sets based on hausdorff distance. Pattern Recogn Lett 25:1603–1611

  18. Lee LW, Chen SM (2008a) Fuzzy multiple attributes group decision-making based on the extension of topsis method and interval type - 2 fuzzy sets. In: Proceedings of 2008 International Conference on Machine Learning and Cybernetics, vol 1-7. IEEE, pp 3260–3265

  19. Lee LW, Chen SM (2008a) A new method for fuzzy multiple attributes group decision-making based on the arithmetic operations of interval type-2 fuzzy sets. In: Proceedings of 2008 International Conference on Machine Learning and Cybernetics, vol 1-7. IEEE, pp 3084–3089

  20. Lin CW, Hong TP (2013) A survey of fuzzy web mining. Data Min Knowl Disc 3(3):190–199

    Article  Google Scholar 

  21. Lin JCW, Li T, Fournier-Viger P, Hong TP, Wu JMT, Zhan J (2016a) Efficient mining of multiple fuzzy frequent itemsets. Intern J Fuzzy Syst:1–9. doi:10.1007/s40815-016-0246-1

  22. Lin JCW, Lv X, Fournier-Viger P, Wu TY, Hong TP (2016b) Efficient Mining of Fuzzy Frequent Itemsets with Type-2 Membership Functions. Springer, Berlin, pp 191–200

  23. Mendel JM (2001) Uncertain rule-based fuzzy logic system: introduction and new directions

  24. Mendel JM, Wu H (2006) Type-2 fuzzistics for symmetric interval type-2 fuzzy sets: Part 1, forward problems. IEEE Trans Fuzzy Syst 14(6):781–792

    Article  Google Scholar 

  25. Mendel JM, Wu H (2007) Type-2 fuzzistics for symmetric interval type-2 fuzzy sets: Part 2, inverse problems. IEEE Trans Fuzzy Syst 15(2):301–308

    Article  Google Scholar 

  26. Mendel JM, John RI, LIu F (2006) Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst 14(6):808–821

    Article  Google Scholar 

  27. Qin J, Liu X (2014) Frank aggregation operators for triangular interval type-2 fuzzy set and its application in multiple attribute group decision making. J Appl Math 2014:Article ID 923,213 24 pages

  28. Singh P (2014) Some new distance measures for type-2 fuzzy sets and distance measure based ranking for group decision making problems. Frontiers of Comput Sci 8(5):741–752

    Article  MathSciNet  Google Scholar 

  29. Szmidt E, Kacprzyk J (2001) Entropy for intuitionistic fuzzy sets. Fuzzy Sets Syst 118(3):467–477

    Article  MathSciNet  MATH  Google Scholar 

  30. Wang W, Liu X, Qin Y (2012) Multi-attribute group decision making models under interval type-2 fuzzy environment. Knowl-Based Syst 30:121–128

    Article  Google Scholar 

  31. Wei CP, Wang P, Zhang YZ (2011) Entropy, similarity measure of interval-valued intuitionistic fuzzy sets and their applications. Inf Sci 181:4273–4286

    Article  MathSciNet  MATH  Google Scholar 

  32. Wu D, Mendel JM (2007) Aggregation using the linguistic weighted average and interval type-2 fuzzy sets. IEEE Trans Fuzzy Syst 15(6):1145–1161

    Article  Google Scholar 

  33. Wu D, Mendel JM (2008) A vector similarity measure for linguistic approximation: Interval type-2 and type-1 fuzzy sets. Inf Sci 178(2):381–402

    Article  MathSciNet  MATH  Google Scholar 

  34. Wu D, Mendel JM (2009) A comparative study of ranking methods, similarity measures and uncertainty measures for interval type-2 fuzzy sets. Inf Sci 179(8):1169–1192

    Article  MathSciNet  Google Scholar 

  35. Xu Z, Chen J (2007) Approach to group decision making based on interval valued intuitionistic judgment matrices. Systems Engineering - Theory and Practice 27(4):126–133

    Article  Google Scholar 

  36. Xu ZS (2007a) Intuitionistic fuzzy aggregation operators. IEEE Trans Fuzzy Syst 15:1179–1187

  37. Xu ZS (2007b) Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making. Control and Decision 22(2):215–219

  38. Xu ZS, Yager RR (2006) Some geometric aggregation operators based on intuitionistic fuzzy sets. Int J Gen Syst 35:417–433

    Article  MathSciNet  MATH  Google Scholar 

  39. Yang MS, Lin DC (2009) On similarity and inclusion measures between type-2 fuzzy sets with an application to clustering. Computers & Mathematics with Applications 57(6):896–907

    Article  MathSciNet  MATH  Google Scholar 

  40. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MATH  Google Scholar 

  41. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning: Part-1. Inf Sci 8:199–251

    Article  MATH  Google Scholar 

  42. Zeng W, Li H (2006) Relationship between similarity measure and entropy of interval-valued fuzzy sets. Fuzzy Sets Syst 157(11):1477–1484

    Article  MathSciNet  MATH  Google Scholar 

  43. Zeng WY, Guo P (2008) NorMalized distance, similarity measure, inclusion measure and entropy of interval-valued fuzzy sets and their relationship. Inf Sci 178:1334– 1342

    Article  MathSciNet  MATH  Google Scholar 

  44. Zhang Z, Zhang S (2013) A novel approach to multi attribute group decision making based on trapezoidal interval type-2 fuzzy soft sets. Appl Math Model 37(7):4948–4971

    Article  MathSciNet  Google Scholar 

  45. Zhou SM, Chiclana F, John RI, Garibaldi JM (2008) Type-2 owa operators: aggregating type-2 fuzzy sets in soft decison making. In: Procedding of the IEEE International Conference on Fuzzy Systems (FUZZ’08), vol 1-5, pp 625–630

  46. Zhou SM, John RI, Chiclana F, Garibaldi JM (2010) On aggregating uncertain information by type-2 owa operators for soft decision making. Int J Intell Syst 25(6):540–558

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Harish Garg.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, S., Garg, H. Distance measures between type-2 intuitionistic fuzzy sets and their application to multicriteria decision-making process. Appl Intell 46, 788–799 (2017). https://doi.org/10.1007/s10489-016-0869-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-016-0869-9

Keywords

Navigation