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
Neighborhood based detection of anomalies in high dimensional spatio-temporal sensor datasets
Proceedings of the 2004 ACM symposium on Applied computing
Finding Spatio-Temporal Patterns in Climate Data Using Clustering
CW '05 Proceedings of the 2005 International Conference on Cyberworlds
ST-DBSCAN: An algorithm for clustering spatial-temporal data
Data & Knowledge Engineering
Discovering interesting sub-paths in spatiotemporal datasets: a summary of results
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Characterizing sensor datasets with multi-granular spatio-temporal intervals
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Statistics-based outlier detection for wireless sensor networks
International Journal of Geographical Information Science
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Neighborhoods, as used for spatial and spatial-temporal data mining, define areas of similarity in data. Unless defined to account for outliers, missing data and spatial-temporal variation, the robustness of methods utilizing neighborhoods will suffer. The focus of this paper is to demonstrate that neighborhoods can be defined and used in a robust manner that is resistant to such challenges. Our approach employs robust methods in both neighborhood construction and neighborhood application to estimate observations. These methods were tested with a large weather sensor data set from the National Weather Service that includes quality control indicators. Results were compared to a popular method used in the weather community, evaluated by root-mean-squared error and grouped by quality control indicator. Our first time published results show that our methods are robust in the presence of outliers, missing data and spatial-temporal variation, yielding results consistent with quality control labels assigned to the data by the provider by way of an extensive rule-based system, indicating that our approaches show promise for use in quality control assessment.