A clustering algorithm based on artificial fish school

For avoiding the dependence of the validity of clustering on the space distribution of high dimensional samples of Fuzzy C2Means, 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 warm 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.