In some real clustering tasks, the data may be sparse and uncertain. Although there is usually some useful knowledge in related scenes, the data among different domains is often of great inconsistency. A new unsupervised transfer learning method is proposed in the framework of belief functions to handle the insufficiency and uncertain problems in clustering simultaneously. Firstly, under the assumption that the source and target domains have the same number of clusters, the Transfer Evidential C Means (TECM) is developed by incorporating the idea of transfer learning and evidential clustering. A novel objective function is designed to employ the cluster prototypes of the source data as references to guide the clustering process on the target. Furthermore, ETECM, as an extended version of TECM, is also introduced for the situation that the two domains have different numbers of clusters. Some experiments conducted on synthetic and real-world data sets demonstrate the advantages of TECM and ETECM.