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

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