关键词: 熵度量, 空间邻域离群点检测, 空间邻域偏离因子, 空间划分

Abstract: There are usually two classes of outlier detection algorithms.One is usually applied to statistical data and takes all attributes as multi-dimensional space，while not distinguish between geo-spatial dimensionality and non-spatial dimensionality in detecting process.Meaningless or incorrect outliers can be found if we use these approaches.The other outlier detection algorithms distinguish between geo-spatial dimensionality and non-spatial dimensionality，but they have poor efficiency or can’t detect neighborhood outliers.To overcome these shortcomings，new approach of spatial neighborhood outliers detection based on entropy measurement is proposed.In this paper，the spatial attributes are used to determine spatial neighborhood，entropy theory is used to determine the weight of non-spatial attributes，and the non-spatial dimensions are used to compute the spatial neighborhood outlier factor，thus spatial neighborhood outliers can be captured. Theoretical analysis shows that the algorithm is reasonable.The experimental results show that the approach is practical.

Key words: entropy measurement, spatial neighborhood outliers detections, spatial outlier factor, space division