SLOM: a new measure for local spatial outliers
Knowledge and Information Systems
Localized Outlying and Boundary Data Detection in Sensor Networks
IEEE Transactions on Knowledge and Data Engineering
Spatial outlier detection in heterogeneous neighborhoods
Intelligent Data Analysis
SOMSO: a self-organizing map approach for spatial outlier detection with multiple attributes
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Spatial neighborhood based anomaly detection in sensor datasets
Data Mining and Knowledge Discovery
Minimum spanning tree based spatial outlier mining and its applications
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Outlier detection with two-stage area-descent method for linear regression
ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
IterativeSOMSO: an iterative self-organizing map for spatial outlier detection
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Spatiotemporal neighborhood discovery for sensor data
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
Mining irregularities in maritime container itineraries
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. Previous work in spatial outlier detection focuses on detecting spatial outliers with a single attribute. In the paper, we propose two approaches to discover spatial outliers with multiple attributes. We formulate the multi-attribute spatial outlier detection problem in a general way, provide two effective detection algorithms, and analyze their computation complexity. In addition, using a real-world census data, we demonstrate that our approaches can effectively identify local abnormality in large spatial data sets.