Abstract
Intuitionistic fuzzy set (IFS) is one of the most robust and trustworthy tools for portraying the imprecise information with the help of the membership degrees. Similarity measure, one of the information measures, plays an important role in treating imperfect and ambiguous information to reach the final decision by determining the degree of similarity between the pairs of the numbers. Motivated by these, this paper aims to present a novel distance/ similarity among the IFSs based on the transformation techniques with their characteristics. To explore the study, the given IFSs are transformed into the right-angled triangle over a unit square area, and hence based on the intersection of the triangles, novel distance and similarity measures are proposed. An algorithm to solve the decision-making problems with the proposed similarity measure is developed and implemented to execute their performance over the numerous examples such as pattern recognition and clustering analysis. The reliability of the developed measure is investigated by applying it in clustering and the pattern recognition problems and their results are compared with some prevailing studies. From the investigation, we conclude that several existing measures fail to give classification results under the different instances such as “division by zero problems” or “counter-intuitive cases” while the proposed measure successfully overcomes this drawback.


Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Zadeh LA. Fuzzy sets. Inf Control. 1965;8:338–53.
Atanassov KT. Intuitionistic fuzzy sets. Fuzzy Sets Syst. 1986;20(1):87–96.
Atanassov K, Gargov G. Interval-valued intuitionistic fuzzy sets. Fuzzy Sets Syst. 1989;31:343–9.
Garg H, Kumar K. Linguistic interval-valued Atanassov intuitionistic fuzzy sets and their applications to group decision-making problems. IEEE Trans Fuzzy Syst. 2019;27(12):2302–11.
Chen SM. Measures of similarity between vague sets. Fuzzy Sets Syst. 1995;74(2):217–23.
Hong DH, Kim C. A note on similarity measures between vague sets and between elements. Inf Sci. 1999;115:83–96.
Dengfeng L, Chuntian C. New similarity measure of intuitionistic fuzzy sets and application to pattern recognitions. Pattern Recognit Lett. 2002;23:221–5.
Mitchell HB. On the dengfeng chuntian similarity measure and its application to pattern recognition. Pattern Recognit Lett. 2003;24:3101–4.
Liang Z, Shi P. Similarity measures on intuitionistic fuzzy sets. Pattern Recognit Lett. 2003;24:2687–93.
Hung WL, Yang MS. Similarity measures of intuitionistic fuzzy sets based on hausdorff distance. Pattern Recognit Lett. 2004;25:1603–11.
Szmidt E, Kacprzyk J. A similarity measure for intuitionistic fuzzy sets and its application in supporting medical diagnostic reasoning. Lect Notes Comput Sci. 2004;3070:388–93.
Wang W, Xin X. Distance measure between intuitionistic fuzzy sets. Pattern Recognit Lett. 2005;26(13):2063–9.
Liu HW. New similarity measures between intuitionistic fuzzy sets and between elements. Math Comput Model. 2005;42:61–70.
Xu ZS. Some similarity meeasures of intuitionistic fuzzy sets and their applications to multiple attribute decision making. Fuzzy Optim Decis Making. 2007;6:109–21.
Song Y, Wang X, Lei L, Xue A. A new similarity measure between intuitionistic fuzzy sets and its application to pattern recognition. Abstr Appl Anal, vol. 2014, pp. Article ID 384 241, 11 pages, 2014.
Chen SM, Cheng SH, Lan TC. A novel similarity measure between intuitionistic fuzzy sets based on the centroid points of transformed fuzzy numbers with applications to pattern recognition. Inf Sci. 2016;343–344:15–40.
Garg H. An improved cosine similarity measure for intuitionistic fuzzy sets and their applications to decision-making process. Hacettepe Journal of Mathematics and Statistics. 2018;47(6):1585–601.
Garg H, Kumar K. An advanced study on the similarity measures of intuitionistic fuzzy sets based on the set pair analysis theory and their application in decision making. Soft Comput. 2018;22(15):4959–70.
Jiang Q, Jin X, Lee SJ, Yao S. A new similarity/distance measure between intuitionistic fuzzy sets based on the transformed isosceles triangles and its applications to pattern recognition. Expert Syst Appl. 2019;116:439–53.
Chen SM, Chang CH. A novel similarity measure between Atanassov’s intuitionistic fuzzy sets based on transformation techniques with applications to pattern recognition. Inf Sci. 2015;291:96–114.
Garg H, Kaur G. Novel distance measures for cubic intuitionistic fuzzy sets and their applications to pattern recognitions and medical diagnosis. Granular Computing. 2020;5(2):169–84.
Vlachos IK, Sergiadis GD. Intuitionistic fuzzy information - application to pattern recognition. Pattern Recognit Lett. 2007;28(2):197–206.
Hung WL, Yang MS. Similarity measures of intuitionistic fuzzy sets based on lp metric. Int J Approx Reason. 2007;46:120–36.
Hung WL, Yang MS. On similarity measures between intuitionistic fuzzy sets. Int J Intell Syst. 2008;23(3):364–83.
Hung WL, Yang MS. On similarity measures between intuitionistic fuzzy sets. Math Comput Model. 2008;23(3):364–83.
Boran FE, Akay D. A biparametric similarity measure on intuitionistic fuzzy sets with applications to pattern recognition. Inf Sci. 2014;255:45–57.
Khan MS, Lohani QD. “A similarity measure for atanassov intuitionistic fuzzy sets, and its application to clustering,” in 2016, International Workshop on Computational Intelligence (IWCI). IEEE. 2016;232–9.
Ngan RT, Ali M, Son LH. Equality of intuitionistic fuzzy sets: a new proximity measure and applications in medical diagnosis. Appl Intell. 2018;48(2):499–525.
Hwang CM, Yang MS, Hung WL, Lee MG. A similarity measure of intuitionistic fuzzy sets based on the Sugeno integral with its application to pattern recognition. Inf Sci. 2012;189:93–109.
Singh S, Garg H. Distance measures between type-2 intuitionistic fuzzy sets and their application to multicriteria decision-making process. Appl Intell. 2017;46(4):788–99.
Garg H. Distance and similarity measure for intuitionistic multiplicative preference relation and its application. Int J Uncertain Quantif. 2017;7(2):117–33.
Xu ZS, Chen J, Wu JJ. Cluster algorithm for intuitionistic fuzzy sets. Inf Sci. 2008;178:3775–90.
Hwang CM, Yang MS, Hung WL. New similarity measures of intuitionistic fuzzy sets based on the jaccard index with its application to clustering. Int J Intell Syst. 2018;33(8):1672–88.
Dhivya J, Sridevi B. A novel similarity measure between intuitionistic fuzzy sets based on the mid points of transformed triangular fuzzy numbers with applications to pattern recognition and medical diagnosis. Applied Mathematics-A Journal of Chinese Universities. 2019;34(2):229–52.
Xu ZS. Intuitionistic fuzzy aggregation operators. IEEE Trans Fuzzy Syst. 2007;15:1179–87.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of Interest
The authors declare that they have no conflict of interest.
Ethical Approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Rights and permissions
About this article
Cite this article
Garg, H., Rani, D. Novel Similarity Measure Based on the Transformed Right-Angled Triangles Between Intuitionistic Fuzzy Sets and its Applications. Cogn Comput 13, 447–465 (2021). https://doi.org/10.1007/s12559-020-09809-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12559-020-09809-2