Spatial Outlier Model and Detection Algorithm with Leapingly Sampling
空间离群点的模型与跳跃取样查找算法

Existing work in outlier detection emphasizes the deviation of non-spatial attribute not only in outlier detecting in statistical database but also in spatial outlier detecting in spatial database.However,both spatial and non-spatial attributes must be synthetically considered in many applications,such as image processing,position-based service.We defined outlier in respect of taking account of both spatial and non-spatial attributes and proposed a new density-based spatial outlier detecting approach with leapingly sampling(DBSODLS).Existing density-based outlier detection approaches must calculate neighborhoods of every object,which are time-consuming.This method makes the best of neighbor information that have been detected,leapingly selects the next object, but not every object,which reduces many neighborhood queries.Theoretical comparison shows this method is better than other density-based methods in efficiency,and the experimental results also show that the approach outperforms the existing density-based methods in efficiency.