Abstract
Outlier detection is an important issue in many industrial and financial applications. Most outlier detection methods suffer from two problems: First, they need parameter tuning in accord to domain knowledge. Second, they are incapable to scale up to high dimensional space. In this paper, we propose a distance-based outlier definition and a detection algorithm ODDC (Distribution Clustering Outlier Detection). We redefine the problem by clustering in the distribution difference space rather than the original feature space. As a result, the new algorithm is stable regardless of different input and scalable to the dimensionality. Experiments on both synthetic and real datasets show that ODDC outperforms the counterpart both in effectiveness and efficiency.
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Niu, K., Huang, C., Zhang, S., Chen, J. (2007). ODDC: Outlier Detection Using Distance Distribution Clustering. In: Washio, T., et al. Emerging Technologies in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77018-3_34
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DOI: https://doi.org/10.1007/978-3-540-77018-3_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77016-9
Online ISBN: 978-3-540-77018-3
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