Shekhar, ShashiLu, Chang-tienZhang, Pusheng2020-09-022020-09-022001-03-08https://hdl.handle.net/11299/215461Identification of outliers can lead to the discovery of unexpected, interesting, and implicit knowledge. Existing methods are designed for detecting spatial outliers in multidimensional geometric data sets, where a distance metric is available. In this paper, we focus on detecting spatial outliers in graph structured data sets. We define tests for spatial outliers in graph structured data sets, analyze the statistical foundation underlying our approach, design a fast algorithm to detect spatial outliers, provide the cost model for outlier detection procedures. In addition, we provide experimental results from the application of our algorithm on a Minneapolis-St. Paul(Twin-Cities) traffic dataset to show its effectiveness and usefulness.en-USReport