Generation of membership functions via possibilistic clustering

Possibilistic clustering has been introduced recently to overcome some of the limitations imposed by the constraint used in the fuzzy c-means algorithm. It was shown that possibilistic memberships correspond more closely to the notion of “typicality”. In this paper, we explore certain interesting properties of possibilistic clustering, In particular, we show that possibilistic clustering can be successfully used to solve two important problems that arise while using fuzzy set theory: i) determination of membership functions, and ii) determination of the number of clusters