The phrase "fuzzy group" is correct and usable in written English.
It can be used in contexts where you are describing a group that is not clearly defined or has ambiguous boundaries.
Example: "In our research, we found that the fuzzy group of participants had varying opinions on the topic."
Alternatives: "ambiguous group" or "vague group".
In [36], aggregation operator of intuitionistic fuzzy group decision making is proposed with the weights of decision makers.
In [38, 43], aggregation of the interval-valued intuitionistic fuzzy group decision making environment with the Choquet integral is studied.
Exploitation of the fuzzy group preference relation is performed solving a multiobjective optimization problem with an evolutionary algorithm.
In this paper, we propose a novel fuzzy group multi-criteria model for EA framework evaluation and selection.
We have applied this operator to the multi-criteria generalized interval-valued trapezoidal fuzzy group decision making with Choquet integral-based TOPSIS.
Moreover, fuzzy group decision making methods should be implemented in order to seek solutions for vague and complex multi-attribute problems in fuzzy environment.
Fuzzy C-Means focuses on finding suitable fuzzy groups for a dataset [7].
They established the basic notions of soft groups as a generalization of the idea of fuzzy groups.
On the other hand, Biswas and Nanda[4], and Rosenfeld[5] worked on rough groups and fuzzy groups, respectively.
Second, a fuzzy grouping genetic algorithm (FGGA) is proposed for grouping components of a design in a modular fashion.
This involves the separation of the hyper-spectral data into a predefined number of fuzzy groups, where each group covers a range of wavelengths and the contribution of each wavelength is modeled by fuzzy membership rules.
