Concept

# Fuzzy set

Summary
In mathematics, fuzzy sets (a.k.a. uncertain sets) are sets whose elements have degrees of membership. Fuzzy sets were introduced independently by Lotfi A. Zadeh in 1965 as an extension of the classical notion of set. At the same time, defined a more general kind of structure called an L-relation, which he studied in an abstract algebraic context. Fuzzy relations, which are now used throughout fuzzy mathematics and have applications in areas such as linguistics , decision-making , and clustering , are special cases of L-relations when L is the unit interval [0, 1]. In classical set theory, the membership of elements in a set is assessed in binary terms according to a bivalent condition—an element either belongs or does not belong to the set. By contrast, fuzzy set theory permits the gradual assessment of the membership of elements in a set; this is described with the aid of a membership function valued in the real unit interval [0, 1]. Fuzzy sets generalize classical sets, since the indicator functions (aka characteristic functions) of classical sets are special cases of the membership functions of fuzzy sets, if the latter only takes values 0 or 1. In fuzzy set theory, classical bivalent sets are usually called crisp sets. The fuzzy set theory can be used in a wide range of domains in which information is incomplete or imprecise, such as bioinformatics. A fuzzy set is a pair where is a set (often required to be non-empty) and a membership function. The reference set (sometimes denoted by or ) is called universe of discourse, and for each the value is called the grade of membership of in . The function is called the membership function of the fuzzy set . For a finite set the fuzzy set is often denoted by Let . Then is called not included in the fuzzy set if (no member), fully included if (full member), partially included if (fuzzy member). The (crisp) set of all fuzzy sets on a universe is denoted with (or sometimes just ).