Spatial neighborhood based anomaly detection in sensor datasets
Data Mining and Knowledge Discovery
Inter-image outliers and their application to image classification
Pattern Recognition
Spatial outlier detection: random walk based approaches
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Two-stage outlier elimination for robust curve and surface fitting
EURASIP Journal on Advances in Signal Processing - Special issue on robust processing of nonstationary signals
Detecting spatio-temporal outliers in crowdsourced bathymetry data
Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
Data Mining and Knowledge Discovery
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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Spatial outliers are the spatial objects whose nonspatial attribute values are quite different from those of their spatial neighbors. Identification of spatial outliers is an important task for data mining researchers and geographers. A number of algorithms have been developed to detect spatial anomalies in meteorologi- cal images, transportation systems, and contagious disease data. In this paper, we propose a set of graph-based algorithms to identify spatial outliers. Our method first constructs a graph based on k-nearest neighbor relationship in spatial domain, as- signs the nonspatial attribute differences as edge weights, and continuously cuts high- weight edges to identify isolated points or regions that are much dissimilar to their neighboring objects. The proposed algorithms have two major advantages compared with the existing spatial outlier detection methods: accurate in detecting point outliers and capable of identifying region out- liers. Experiments conducted on the US Housing data demon- strate the effectiveness of our proposed algorithms.