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
Detecting graph-based spatial outliers: algorithms and applications (a summary of results)
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th 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
A Unified Approach to Detecting Spatial Outliers
Geoinformatica
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
Detecting Spatial Outliers with Multiple Attributes
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Detecting and tracking regional outliers in meteorological data
Information Sciences: an International Journal
Spatial Outlier Detection: A Graph-Based Approach
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Discovering Flow Anomalies: A SWEET Approach
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Spatial contextual classification and prediction models for mining geospatial data
IEEE Transactions on Multimedia
Probabilistic distance based abnormal pattern detection in uncertain series data
Knowledge-Based Systems
An Empirical Evaluation of Similarity Coefficients for Binary Valued Data
International Journal of Data Warehousing and Mining
Detecting spatio-temporal outliers in crowdsourced bathymetry data
Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
Success of anomaly detection, similar to other spatial data mining techniques, relies on neighborhood definition. In this paper, we argue that the anomalous behavior of spatial objects in a neighborhood can be truly captured when both (a) spatial autocorrelation (similar behavior of nearby objects due to proximity) and (b) spatial heterogeneity (distinct behavior of nearby objects due to difference in the underlying processes in the region) are taken into consideration for the neighborhood definition. Our approach begins by generating micro neighborhoods around spatial objects encompassing all the information about a spatial object. We selectively merge these based on spatial relationships accounting for autocorrelation and inferential relationships accounting for heterogeneity, forming macro neighborhoods. In such neighborhoods, we then identify (i) spatio-temporal outliers, where individual sensor readings are anomalous, (ii) spatial outliers, where the entire sensor is an anomaly, and (iii) spatio-temporally coalesced outliers, where a group of spatio-temporal outliers in the macro neighborhood are separated by a small time lag indicating the traversal of the anomaly. We demonstrate the effectiveness of our approach in neighborhood formation and anomaly detection with experimental results in (i) water monitoring and (ii) highway traffic monitoring sensor datasets. We also compare the results of our approach with an existing approach for spatial anomaly detection.