Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
A spatial data mining method by Delaunay triangulation
GIS '97 Proceedings of the 5th ACM international workshop on Advances in geographic information systems
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Triangle: Engineering a 2D Quality Mesh Generator and Delaunay Triangulator
FCRC '96/WACG '96 Selected papers from the Workshop on Applied Computational Geormetry, Towards Geometric Engineering
Spatial Data Mining: A Database Approach
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
DM-AMS: employing data mining techniques for alert management
dg.o '05 Proceedings of the 2005 national conference on Digital government research
Agency interoperation for effective data mining in border control and homeland security applications
dg.o '04 Proceedings of the 2004 annual national conference on Digital government research
dg.o '04 Proceedings of the 2004 annual national conference on Digital government research
Enhancing Security and Privacy in Traffic-Monitoring Systems
IEEE Pervasive Computing
Detecting and tracking regional outliers in meteorological data
Information Sciences: an International Journal
Hierarchical Classifiers for Complex Spatio-temporal Concepts
Transactions on Rough Sets IX
Spatial outlier detection in heterogeneous neighborhoods
Intelligent Data Analysis
A multi-relational approach to spatial classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Anomaly detection and spatio-temporal analysis of global climate system
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Spatial outlier detection: random walk based approaches
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Spatial categorical outlier detection: pair correlation function based approach
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A fuzzy index for detecting spatiotemporal outliers
Geoinformatica
Spatiotemporal neighborhood discovery for sensor data
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
An Empirical Evaluation of Similarity Coefficients for Binary Valued Data
International Journal of Data Warehousing and Mining
Mining robust neighborhoods for quality control of sensor data
Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming
A Domain Knowledge as a Tool For Improving Classifiers
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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The behavior of spatial objects is under the influence of nearby spatial processes. Therefore in order to perform any type of spatial analysis we need to take into account not only the spatial relationships among objects but also the underlying spatial processes and other spatial features in the vicinity that influence the behavior of a given spatial object. In this paper, we address the outlier detection by refining the concept of a neighborhood of an object, which essentially characterizes similarly behaving objects into one neighborhood. This similarity is quantified in terms of the spatial relationships among the objects and other semantic relationships based on the spatial processes and spatial features in their vicinity. These spatial features could be natural such as a stream, and vegetation, or man-made such as a bridge, railroad, and chemical factory. The paper also addresses the identification of spatio-temporal outliers in high dimensions, in their neighborhood.