Generalization of Fuzzy C-Means Based on Neutrosophic Logic

This article presents a New Neutrosophic C-Means (NNCMs) method for clustering. It uses the neutrosophic logic (NL), to generalize the Fuzzy C-Means (FCM) clustering system. The NNCMs system assigns objects to clusters using three degrees of membership: a degree of truth, a degree of indeterminacy, and a degree of falsity, rather than only the truth degree used in the FCM. The indeterminacy degree, in the NL, helps in categorizing objects laying in the intersection and the boundary areas. Therefore, the NNCMs reaches more accurate results in clustering. These degrees are initialized randomly without any constraints. That is followed by calculating the clusters’ centers.