Incomplete data set is a common problem in clustering analysis. It is quite important to process the missing data which is the key factor impacting the clustering performance. In view of the problem that the Fuzzy C-means (FCM) algorithm is not directly applicable to the case of incomplete data, a FCM clustering algorithm for incomplete data sets based on improved BP to estimate the missing attributes is proposed in this paper. The improved BP can be trained by incomplete data sets, and we adopt the nearest-neighbor rule to select training samples for missing attributes. Training samples are composed by complete samples and incomplete samples of the incomplete data set, which make full use of data set information and can obtain more accuracy imputations of missing attributes. The experimental results for several UCI data sets demonstrate that the proposed method for clustering of incomplete data sets has a good effect.