As general concepts of sup-hesitant fuzzy right (resp., left, interior, two-sided) ideals semigroups, the sup+α-hesitant and sup-β-hesitant are introduced their properties investigated. Then, established by sets, Łukasiewicz anti-fuzzy Pythagorean hesitant hybrid interval-valued sets cubic sets. Finally, we characterize which is intra-regular, completely regular, simple semigroups or another ty...

Clustering algorithms have been widely used artificial intelligence, data mining and machine learning, etc. It is unsupervised classification and is divided into groups according to data sets. That is, the data sets of similarity partition belong to the same group; otherwise data sets divide other groups in the clustering algorithms. In general, to analysis interval data needs Type II fuzzy log...

Here are many situations in real applications of decision making where we deal with uncertain conditions.  Due to the different sources of uncertainty,  since its original definition of fuzzy sets in 1965 cite{zadeh1965},  different generalizations and extensions of fuzzy sets have been introduced: Type-2 fuzzy sets cite{6,13}, Intuitionistic fuzzy sets cite{1}, fuzzy multi-sets cite{37} and et...

Moreover, Zhu et al. [11], developed dual hesitant fuzzy sets (DHFSs) as a new extension of HFSs. The DHFS is a comprehensive set encompassing several existing fuzzy sets with certain conditions, whose membership and nonmembership are represented by a set of possible values respectively. In particular cases, DHFSs can reduce to some existing fuzzy sets, such as FSs, IFSs, HFSs and FMSs. From a ...

As Granular Computing has gained interest, more research has lead into using different representations for Information Granules, i.e., rough sets, intervals, quotient space, fuzzy sets; where each representation offers different approaches to information granulation. These different representations have given more flexibility to what information granulation can achieve. In this overview paper, ...