提出了一种基于聚类的空间异常探测方法。该方法通过空间聚类获得局部相关性较强的实体集合, 分别探测空间异常, 给出了一种稳健的空间异常度量指标, 提高了异常探测结果的可靠性。通过实例验证以及与SOM方法的比较分析, 证明了该方法的正确性和优越性。

Spatial outlier detection has been a hot issue in the field of spatial data mining and knowledge discovery. Spatial outliers may be utilized to discover and predict the potential change laws or development tendency of geographical phenomenon in the real world. Among the existing spatial outlier detection methods, there are mainly two aspects of issues. On the one hand, these methods primarily consider that all the entities for outlier detection are correlated. Actually, spatial correlation decreases with the increase of distance. Entities will become independent with each other at a distance of rang. Thus, current methods can only discover the obviously outliers in the whole, some local outliers may not be detected. On the other hand, the spatial outlier measures are not enough robust, which are seriously influenced by the construction process of spatial neighborhoods of spatial entities and the possible outliers in spatial neighborhoods. To overcome these two limitations, spatial clustering as a means is firstly employed to extract the local autocorrelation patterns, called clusters. Then, a robust spatial outlier measure is proposed to determine spatial outliers in each cluster. This method is able to detect spatial outliers more accurately. Finally, a practical ex-ample is utilized to demonstrate the validity of the spatial outlier detection method proposed in this paper. The comparative experiment is also provided to further demonstrate the method in this paper to be superior to classic SOM method.