Abstract: Identifying the rare instances in datasets can lead to the discovery of unexpected and useful knowledge. However, existing algorithms for such outlier detection applications are not efficient when facing large datasets. With detailed discussion on the futures of outliers in datasets, a novel grid-based algorithm, called GridOF, is presented, which first filters out crowded grids and then finds outliers by computing adjusted mean approximation of the density function. While still keeping desirable outlier detection accuracy, the algorithm has a very high performance in both space and time usage. Results of experiments also demonstrate promising availabilities of this approach.