Gustafson- Kessel( GK) 聚类算法可以有效地搜索超椭球、平面和线型的数据类，但仍然存在对初始聚类中心较敏感、易于陷入局部最优的缺陷．为此，文中根据鱼群觅食与聚类的相似性，利用人工鱼群( AFS) 算法对聚类中心进行初始化，提出了改进的G- K 聚类算法，并利用人工数据集和IRIS 数据集进行仿真研究．结果表明，文中算法能有效地发现数据集中的聚类结构，聚类效果优于GK 聚类算法．

Gustafson- Kessel (GK) clustering algorithm can be used to search for hyper ellipsoid,plane and lineardata effectively,but it is still sensitive to initial clustering center and easy to fall into a local optimum.In order tosolve this problem,this paper presents an improved GK clustering algorithm,which adopts the artificial fish- swarm(AFS) algorithm to initialize clustering centers according to the similarity between clustering and searching food offish school.Then,this algorithm is employed to perform a simulation based on artificial data and IRIS data.Theresults show that the improved algorithm can find the clustering structure of datasets effectively,and it possesses abetter classification performance than GK clustering algorithm.