Abstract
Outlier mining is an important branch of data mining and has attracted much attention recently. The density-based method LOF is widely used in application. However, selecting MinPts is non-trivial, and LOF is very sensitive to its parameters MinPts. In this paper, we propose a new outlier detection method based on Voronoi diagram, which we called Voronoi based Outlier Detection (VOD). The proposed method measures the outlier factor automatically by Voronoi neighborhoods without parameter, which provides highly-accurate outlier detection and reduces the time complexity from O(n 2) to O(nlogn).
Supported by the Science and Technology Key Projects of Shandong Province under Grant No.2007GG3WZ10010; Doctoral Scientific Research Foundation of Shandong University of Finance under Grant No.06BSJJ09.
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Qu, J. (2008). Outlier Detection Based on Voronoi Diagram. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_51
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DOI: https://doi.org/10.1007/978-3-540-88192-6_51
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