Abstract
Identification of outliers can lead to the discovery of unexpected and interesting 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 statistical tests, analyze the statistical foundation underlying our approach, design a fast algorithm to detect spatial outliers, and provide cost models for outlier detection procedures. In addition, we provide experimental results from the application of our algorithm on a Minneapolis-St. Paul (Twin Cities) traffic data set to show its effectiveness and usefulness.