Double fuzzy C-means model and its application in the technology innovation of China
Li Li, Renxiang Wang, Xican Li · Journal of Intelligent & Fuzzy Systems · 2016
Aiming to improve the algorithm of the classic fuzzy C-means model (FCM), a double fuzzy C-means model (DFCM) was presented in this paper. A new fuzzy cluster validity index ( RWW ) and the DFCM algorithm were proposed, simultaneously. Then, the double fuzzy C-means model was applied for the clustering analysis of the regional technology innovation level in China. The validity of the double fuzzy C-means model was tested using the wine data set of UCI. The comparison results of different cluster validity indexes validated the fuzzy cluster validity index ( RWW ) proposed in this paper. The application example and wine data set clustering results indicated that the DFCM model enhanced the intra-class compactness and inter-class separation, making the classification more accurate.