Background:Ab initio protein structure prediction is to predict the tertiary structure of a protein from its amino acid sequence alone.As an important topic in bioinformatics,considerable efforts have been made on designing the ab initio methods.Unfortunately,lacking of a perfect energy function,it is a difficult task to select a good near-native structure from the predicted decoy structures in the last step.Methods:Here we propose an ensemble clustering method based on k-medoids to deal with this problem.The k-medoids method is run many times to generate clustering ensembles,and then a voting method is used to combine the clustering results.A confidence score is defined to select the final near-native model,considering both the cluster size and the cluster similarity.Results:We have applied the method to 54 single-domain targets in CASP-11.For about 70.4％ of these targets,the proposed method can select better near-native structures compared to the SPICKER method used by the I-TASSER server.Conclusions:The experiments show that,the proposed method is effective in selecting the near-native structure from decoy sets for different targets in terms of the similarity between the selected structure and the native structure.