Quick spatial outliers detecting with random sampling

Authors:
Tianqiang Huang;Xiaolin Qin;Qinmin Wang;Chongcheng Chen
Affiliations:
Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China;Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China;Spatial Information Research Center in Fujian Province, Fuzhou, China;Spatial Information Research Center in Fujian Province, Fuzhou, China
Venue:
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

Existing Density-based outlier detecting approaches must calculate neighborhood of every object, which operation is quite time-consuming The grid-based approaches can detect clusters or outliers with high efficiency, but the approaches have their deficiencies We proposed new spatial outliers detecting approach with random sampling This method adsorbs the thought of grid-based approach and extends density-based approach to quickly remove clustering points, and then identify outliers It is quicker than the approaches based on neighborhood queries and has higher precision The experimental results show that our approach outperforms existing methods based on neighborhood query.