MicroRNAs (miRNAs) play an important role in the process of life. In recent years, predicting the associations between miRNAs and diseases has become a hot topic in research. Existing computational methods can be mainly divided into two categories:methods based on similarity measurement, and methods based on machine learning. The former approaches predict miRNA-disease associations by measuring similarity of nodes in the biological networks, but they need to build high quality biological networks. The latter approaches apply machine learning algorithms to this problem, but they need to build a negative collection of high credibility. To address those shortcomings, this paper presents a novel computational model called BNPDCMDA (bipartite network projection based on density clustering to predict miRNA-disease associations) to predict miRNAs-disease associations. First, a miRNA-disease double-layer network model is constructed. Then, similarity of miRNAs is used to perform density clustering. Next, bipartite network projection is applied to miRNA-disease double-layer composed of density clustered miRNAs and disease sets. Finally, predictions for miRNA-disease association are performed. Further experimental results show that the proposed approach achieves AUC of 99.08% by using the leave-one-out cross-validation test, which demonstrates better predictive performance of BNPDCMDA than other methods. Moreover, certain miRNAs associated common diseases are predicted by BNPDCMDA.