Hyperlink prediction aims to utilize inherent properties of observed network to reproduce the missing links in the network. Existing hyperlink prediction algorithms often make predictions based on entire network， and some link types with insufficient training samples data may be missed， resulting in imcomplete link types to be detected. To address this problem， a clustering-based hyperlink prediction algorithm named C-CMM was proposed. Firstly， the dataset was divided into clusters， and then the model was constructed for each cluster to perform hyperlink prediction. The proposed algorithm can make full use of the information contained in the observation samples of each cluster， and widen the coverage range of the prediction results. Experimental results on three real-world datasets show that the proposed algorithm outperforms a great number of state-of-the-art link prediction algorithms in prediction accuracy and efficiency， and has the prediction coverage more comprehensive.