%0 Journal Article
%A LIU Cangsheng
%A XU Qinglin
%T Fuzzy C-means clustering algorithm based on density peak value optimization
%D 2018
%R 10.3778/j.issn.1002-8331.1703-0228
%J Computer Engineering and Applications
%P 153-157
%V 54
%N 14
%X Aiming at the traditional fuzzy C-means clustering algorithm and the fuzzy C-means algorithm based on K-means++ optimization clustering center, with the defects that initial clustering center sensitivity, clustering speed convergence is slow, the clustering algorithm needs to be given the number of artificial clustering, inspired by CFSFDP, a fuzzy C-means clustering algorithm based on density peak algorithm optimization is proposed. Adaptive clustering algorithm is generated to determine the number of clusters and to optimize the number of clusters. The clustering algorithm is based on the fast clustering algorithm（CFSFDP） convergence process. The experimental results show that the improved algorithm can accurately obtain the number of clusters and improve the performance compared with the traditional fuzzy clustering C - means algorithm, and accelerate the convergence speed of the algorithm to achieve a relatively better clustering effect.
%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1703-0228