Abstract: Classic fuzzy c-means algorithm（FCM） is based on Euclidean distance, it includes a problem that different size of class cluster is not clustered correctly. Aiming at the problem, this paper presents a fuzzy C-means algorithm based on the membership degree of K-nearest neighbor（KNN_FCM）. Then the paper discusses rough C-means clustering algorithm and rough fuzzy C-means clustering algorithm, which are both based on the membership degree of K-nearest neighbor. These algorithms avoide a question of threshold selection in traditional rough C-means clustering algorithm and rough fuzzy C-means clustering algorithm. Compare the KNN_FCM、KNN_RCM、KNN_RFCM with FCM、RFM、RFCM in UCI dataset, the experimental result shows that the method is feasible and effective.

Key words: membership degree of K-nearest neighbor, clustering, fuzzy C-means, rough C-means, rough fuzzy C-means

摘要： 经典模糊C均值聚类算法（FCM）基于欧氏距离，存在不同规模类簇不能正确聚类问题，针对此问题提出一种基于[K]近邻隶属度的模糊C均值聚类算法（KNN_FCM）。讨论了基于[K]近邻隶属度的粗糙C均值聚类算法（KNN_RCM）和粗糙模糊C均值聚类算法（KNN_RFCM），此方法避免了传统粗糙C均值聚类算法（RCM）和粗糙模糊C均值聚类算法（RFCM）中阈值选择问题。将KNN_FCM、KNN_RCM、KNN_RFCM分别与FCM、RFM、RFCM在UCI数据集上进行仿真比较，结果表明新方法是可行、有效的。

关键词: K近邻隶属度, 聚类, 模糊C均值, 粗糙C均值, 粗糙模糊C均值