Abstract: Outlier detection is an important branch in the area of data mining It has been widely applied in fraud detection, loan approval, weather prediction and customer segmentation The former algorithms for outlier detection are only applied in static environment So recalculation is needed when updates occur In this paper, the first incremental outlier mining algorithm IncLOF is presented in dynamic environment based on LOF which is an algorithm for identifying density based local outliers IncLOF only recalculates a fraction of data affected by insertion or deletion, so it can greatly speed up outlier mining The results from a study on synthetic data sets demonstrate that the runtime of IncLOF is much less than LOF in dynamic environment, especially when the ratios of MinPts to database size are small