Abstract
Existing work in outlier detection emphasizes the deviation of non-spatial attribution not only in statistical database but also in spatial database. However, both spatial and non-spatial attributes must be synthetically considered in many applications. The definition synthetically considered both was presented in this paper. New Density-based spatial outliers detecting with stochastically searching approach (SODSS) was proposed. This method makes the best of information of neighborhood queries that have been detected to reduce many neighborhood queries, which makes it perform excellently, and it keeps some advantages of density-based methods. Theoretical comparison indicates our approach is better than famous algorithms based on neighborhood query. Experimental results show that our approach can effectively identify outliers and it is faster than the algorithms based on neighborhood query by several times.
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© 2005 Springer-Verlag Berlin Heidelberg
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Huang, T., Qin, X., Chen, C., Wang, Q. (2005). Density-Based Spatial Outliers Detecting. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds) Computational Science – ICCS 2005. ICCS 2005. Lecture Notes in Computer Science, vol 3514. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11428831_122
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DOI: https://doi.org/10.1007/11428831_122
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26032-5
Online ISBN: 978-3-540-32111-8
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