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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Shekhar, S., Lu, C.T., Zhang, P.: A unified approach to detecting spatial outliers. GeoInformatica 7, 2, 139–166 (2003)
Lu, C.T., Chen, D., Kou, Y.: Algorithms for spatial outlier detection. In: Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), Melbourne, Florida, USA, December 19-22, pp. 597–600. IEEE Computer Society, Los Alamitos (2003)
Barnett, V., Lewis, T.: Outliers in Statistical Data. John Wiley, Chichester (1994)
Johnson, T., Kwok, I., Ng, R.T.: Fast computation of 2-dimensional depth contours. In: Proc. KDD 1998, pp. 224–228 (1998)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases. In: Proceedings of KDD 1996, Portland OR, USA, pp. 226–231 (1996)
Knorr, E., Ng, R.: Algorithms for Mining Distance-Based Outliers in Large Datasets. In: Proc. 24th VLDB Conference (1998)
Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: Identifying density-based local outliers. In: Proceedings of SIGMOD 2000, Dallas, Texas, pp. 427–438 (2000)
Hu, T., Sung, S.Y.: Detecting pattern-based outliers. Pattern Recognition Letters 24, 3059–3068 (2003)
He, Z., Xu, X., Deng, S.: Discovering cluster-based local outliers. Pattern Recognition Letters 24, 1642–1650 (2003)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications. Data Mining and Knowledge Discovery 2, 2, 169–194 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Huang, T., Qin, X., Wang, Q., Chen, C. (2005). Quick Spatial Outliers Detecting with Random Sampling. In: Kégl, B., Lapalme, G. (eds) Advances in Artificial Intelligence. Canadian AI 2005. Lecture Notes in Computer Science(), vol 3501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424918_32
Download citation
DOI: https://doi.org/10.1007/11424918_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25864-3
Online ISBN: 978-3-540-31952-8
eBook Packages: Computer ScienceComputer Science (R0)