Outlier ensemble is an important methodology for improving outlier detection, but faces severe challenges in unsupervised settings. Unlike traditional outlier ensembles which revised scores by considering only the values of the scores from multiple detectors, we present a novel Regional Ensemble (RE). RE combines the scores from multiple objects and multiple detectors and simultaneously takes into consideration both the values and the distribution of these scores. RE specifically enhances the score of a given object by using the scores of neighboring objects of the given object, under the assumption that the scores of the majority of neighboring objects are reliable. RE provides many potential applications, particularly in data mining and machine learning. Compared to existing outlier ensembles with 30 real-world datasets tested, RE attained the best performance with 14 datasets, while the current standard achieves superior performance with only 8 datasets. RE can significantly improve the best existing from 0.83 to 0.86 AUC on average.