Intuitionistic Fuzzy Similarity Measures Research Articles

Intuitionistic fuzzy similarity measure: Theory and applications

Intuitionistic fuzzy similarity measure: Theory and applications

Intuitionistic fuzzy similarity measures based on Frank t-norms family

Intuitionistic fuzzy sets, proposed by Atanassov, have gained attention of many researches for applications in various fields. Different types of similarity measures for intuitionistic fuzzy sets were proposed and used in numerous applications. In this paper we extend some crisp cardinality measures to measures for intuitionistic fuzzy sets; the main role in this process is played by the Frank t-norm family.

An Integrated Intuitionistic Fuzzy Similarity Measures for Medical Problems

AbstractThe purpose of this paper is to develop an integrated similarity measures model based on intuitionistic fuzzy sets. This integrated model has improved two similarity measures methods: (1) Ye (Mathematical and Computer Modelling, 53, 91-97 (2011)) presented a novel cosine similarity measures method for handling pattern recognition problems based on intuitionistic fuzzy sets. However, in some cases, Ye's method can not give sufficient information to discriminate a sample between two patterns. Therefore, we provide an improved method for the similarity measure. (2) Hung et al. (Computer-Aided Design, 40, 447-454 (2008)) provided a new score function to measure the degree of suitability of each alternative. In this paper, we extend their method to modify the hesitation parameter with rate operations as a defuzzfication function for each characteristic, and then the defuzzy results as a parameter input new similarity measure method based on the Minkowski distance conception. Finally, the proposed simil...

A spectral clustering algorithm based on intuitionistic fuzzy information

In this paper, we propose a technique for clustering data with intuitionistic fuzzy information. We first define two new intuitionistic fuzzy similarity measures, and then use it to construct an intuitionistic fuzzy similarity measure matrix, by which we present a spectral algorithm to cluster intuitionistic fuzzy information. At last, two numerical examples are given to illustrate and verify our algorithm.

Pattern Recognition Based on New Distances between Intuitionistic Fuzzy Sets

In the application of intuitionistic fuzzy sets(IFSs), distances and similarity measures play very important roles. In this paper, firstly, the modified definition of normalized distance and degree of similarity between IFSs are introduced, which are proved to be more reasonable than some existing definitions. Then, the relations between normalized distance and degree of similarity are analyzed. New distances and similarity measures between IFSs are proposed and corresponding proofs are given. Finally, a comparison of application to pattern recognitions is made to show the proposed distances and similarity measures are more reasonable than some existing methods.