LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Unified Approach to Detecting Spatial Outliers
Geoinformatica
Algorithms for Spatial Outlier Detection
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Detecting Spatial Outliers with Multiple Attributes
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Detecting graph-based spatial outliers
Intelligent Data Analysis
A minimum spanning tree-inspired clustering-based outlier detection technique
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
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Spatial outliers are spatial objects whose nonspatial attributes are significantly different from the values of their neighborhoods. Detection of spatial outliers will provide the user with meaningful, interesting and potential information. Usually, algorithms for outlier mining on traditional business-oriented datasets are no longer applicable to spatial datasets. A new algorithm based on MST clustering is proposed in this paper to identify spatial outliers. The algorithm organically integrates the approach of minimum spanning trees and the density-based mechanism for outlier mining. Basic spatial structure characteristics of spatial objects are maintained by Delaunay Triangles and MST clustering is achieved by cutting off several most inconsistent edges. It turns out that the algorithm can find true spatial outliers, and it doesnt require any parameter for the algorithm be specified firstly. Experiments on real application problems indicate that the proposed algorithm is feasible and effective for identifying outliers from the large-scale spatial datasets.