Spatial outlier detection

Abstract

A spatial outlier is a spatially referenced object whose non-spatial attributes are very different from those of its spatial neighbors. Spatial outlier detection has been an important part of spatial data mining and attracted attention in the past decades. Numerous SOD (Spatial Outlier Detection) approaches have been proposed. However, in these techniques, there exist the problems of masking and swamping. That is, some spatial outliers can escape the identification, and normal objects can be erroneously identified as outliers. In this paper, two Random walk based approaches, RW-BP (Random Walk on Bipartite Graph) and RW-EC (Random Walk on Exhaustive Combination), are proposed to detect spatial outliers. First, two different weighed graphs, a BP (Bipartite graph) and an EC (Exhaustive Combination), are modeled based on the spatial and/or non-spatial attributes of the spatial objects. Then, random walk techniques are utilized on the graphs to compute the relevance scores between the spatial objects. Using the analysis results, the outlier scores are computed for each object and the top k objects are recognized as outliers. Experiments conducted on the synthetic and real datasets demonstrated the effectiveness of the proposed approaches.