Dynamic fuzzy clustering method based on artificial fish swarm algorithm
基于人工鱼群算法的动态模糊聚类

In order to avoid the dependence of the validity of clustering on the space distribution of high dimensional samples of Fuzzy C-Means (FCM), a dynamic fuzzy clustering method based on artificial fish swarm algorithm was proposed. By introducing a fuzzy equivalence matrix to the similar degree among samples, the high dimensional samples were mapped to two dimensional planes. Then the Euclidean distance of the samples was approximated to the fuzzy equivalence matrix gradually by using artificial fish swarm algorithm to optimize the coordinate values. Finally, the fuzzy clustering was obtained. The proposed method, not only avoided the dependence of the validity of clustering on the space distribution of high dimensional samples, but also raised the clustering efficiency. Experiment results show that it is an efficient clustering algorithm with rapid speed and high precision.