Clustering algorithm for intuitionistic fuzzy sets

By Zeshui Xu, Jing Chen, Junjie Wu. Information Sciences, 2008

450 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/be7ae111038c3721305a80aea40818c93f602d83

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zeshui Xu
2. Meimei Xia
3. Xindong Peng
4. Yong Yang
5. Na Chen
6. Bahram Farhadinia
7. José M. Merigó
8. Huiyong Yuan

In Computational Theory and Mathematics

1. Bahram Farhadinia
2. Xindong Peng
3. Meimei Xia
4. Yong Yang
5. Zeshui Xu
6. Na Chen
7. José M. Merigó
8. Huiyong Yuan

In Management Science and Operations Research

1. Zeshui Xu
2. Xindong Peng
3. Meimei Xia
4. Bahram Farhadinia
5. José M. Merigó
6. Yong Yang
7. Weize Wang
8. Na Chen

Citations in the corpus

• Intuitionistic Fuzzy Information Aggregation Using Einstein Operations (2012, background): “Since the appearance of AIFS in 1986 [2], several researchers [6], [16], [39], [60], [62], [63] have made important contributions to the development of AIFS theory and its applications, resulting in the great success from the theoretical and technological points of view.”
• Distance and similarity measures for hesitant fuzzy sets (2011, background): “Later, a number of other extensions of the above distance measures have been developed for linguistic fuzzy sets (LFSs) [38] and IFSs [10–12,15–17,24,29,30,35,39–44].”
• New decision-making techniques and their application in the selection of financial products (2010, methodology): “Note that these aggregation operators can be used in a lot of other problems previously studied with the NHD [20], such as the calculation of distances between intuitionistic fuzzy sets [39,44,45] and interval-valued fuzzy sets.”
• Choquet integrals of weighted intuitionistic fuzzy information (2010, methodology): “Since it was first introduced in 1986, IFS theory has been widely investigated and applied to a variety of fields [2,4,12,13,20,22].”
• Pythagorean Fuzzy Information Measures and Their Applications (2017, methodology): “Based on Xu et al., 59 and similarity measures developed previously for PFSs, in what follows, we propose a clustering algorithm under Pythagorean fuzzy environments.”
• Information measures for hesitant fuzzy sets and interval-valued hesitant fuzzy sets (2013, background): “[34] and therefore it is not necessary to be repeated here.”
• TOPSIS Method Based on Correlation Coefficient under Pythagorean Fuzzy Soft Environment and Its Application towards Green Supply Chain Management (2021, methodology): “[66] deal with uncertainty by using MD and NMD.”
• On distance and correlation measures of hesitant fuzzy information (2011, methodology): “Xu et al. 24 utilized a set-theoretic approach 25 to derive the association coefficients of intuitionistic fuzzy sets taking into account all the three terms (the membership degree, nonmembership degree, and hesitation margin, describing an intuitionstic fuzzy set.”
• Correlation coefficients of hesitant fuzzy sets and their applications to clustering analysis (2013, methodology): “In what follows we give a new formula of calculating the correlation coefficient, which is similar to that used in IFSs [21]:”