An extensional fuzzy c-means clustering algorithm based on intuitionistic extension index
Hsiang-Chuan Liu, Yen-Kuei Yu, Hsien‐Chang Tsai, Tungsheng Liu, Bai-Cheng Jeng · 2011
In this paper, a novel fuzzy c-means algorithm based on an intuitionistic extension index for any n-dimensional point set, namely the E-FCM algorithm, is being proposed. If the intuitionistic extension index is equal to 0, then the proposed new algorithm is just the traditional fuzzy c-means algorithm (FCM), in other words, the E-FCM algorithm is a generalization of the FCM algorithm. It is quite different from Xu and Wu's intuitionistic fuzzy C-means clustering algorithm (IFCM algorithm), since the latter can only be used for intuitionistic fuzzy sets, but not for any n-dimensional point set. The experimental results of three benchmark data sets show that the proposed E-FCM algorithm outperforms the FCM algorithm.