A unified approach to detecting spatial outliers
Spatial outliers represent locations which are significantly different from their neighborhoods even though they may not be significantly different from the entire population. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and implicit knowledge, such as local instability. In this paper, we first provide a general definition of S-outhers for spatial outliers. This definition subsumes the traditional definitions of spatial outliers. Second, we characterize the computation structure of spatial outlier detection methods and present scalable algorithms. Third, we provide a cost model of the proposed algorithms. Finally, we experimentally evaluate our algorithms using a Minneapolis-St. Paul (Twin Cities) traffic data set.
outlier detection spatial data mining scalable algorithm for outlier detection