Outlier detection can lead to discovering unexpected and interesting knowledge, which is critically important to some areas such as monitoring of criminal activities in electronic commerce, credit card fraud, and the like. In this paper, we propose an efficient outlier detection method with clusters as by-product, which works efficiently for large datasets. Our contributions are: a) We introduce a Local Connective Factor (LCF); b) Based on LCF, we propose an outlier detection method which can efficiently detect outliers and group data into clusters in a one-time process. Our method does not require the beforehand clustering process, which is the first step in other state-of-the-art clustering-based outlier detection methods; c) The performance of our method is further improved by means of a vertical data representation, P-trees¹. We tested our method with real dataset. Our method shows around five-time speed improvements compared to the other contemporary clustering-based outlier-detection approaches.