中文摘要：

隶属度修正是模糊C-均值聚类算法改进的一个重要方向,该类改进算法引入模糊阈值修正隶属度,极大的加快了算法的收敛.然而其模糊阈值的自适应取值一直是一个较难解决的问题.针对这个问题,从数据对聚类中心的物理吸引和相似关系等角度提出了一种针对隶属度修正类FCM算法的模糊阈值参数选择方法,并从该参数选择公式的单调性、收敛性和鲁棒性等角度理论验证了该方法的有效性.仿真实验表明,该参数选择方法有效并具有较好的自适应效果,在加入离群点时也有着较强的鲁棒性,对于隶属度修正类FCM算法的参数选择有着较高的应用价值.

英文摘要：

Membership correction is an important direction in the improvement of fuzzy c-means clustering algorithm. This type of improved algorithms introduce fuzzy threshold to correct membership value, which greatly speed up the algorithm convergence. However, the adaptive value of fuzzy threshold is always a difficult problem. To solve the problem, a method is presented to select the parameter of fuzzy threshold based on similarity relation and physical attraction between data and clustering centers. The monotonicity, convergence and robustness of the parameter selection formula are discussed to verify the effectiveness of this method. Simulation shows that the parameter selection method is effective, adaptive and robust, which has high application value to parameter selection of membership modified FCM algorithms.