2008 AnglebasedOutlierDetectioninHig

• (Kriegel et al., 2008) ⇒ Hans-Peter Kriegel, Matthias S hubert, and Arthur Zimek. (2008). “Angle-based Outlier Detection in High-dimensional Data.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401946

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• http://scholar.google.com/scholar?q=%22Angle-based+outlier+detection+in+high-dimensional+data%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401946&preflayout=flat#citedby

Quotes

Abstract

Detecting outliers in a large set of data objects is a major data mining task aiming at finding different mechanisms responsible for different groups of objects in a data set. All existing approaches, however, are based on an assessment of distances (sometimes indirectly by assuming certain distributions) in the full-dimensional Euclidean data space. In high-dimensional data, these approaches are bound to deteriorate due to the notorious "curse of dimensionality". In this paper, we propose a novel approach named ABOD (Angle-Based Outlier Detection) and some variants assessing the variance in the angles between the difference vectors of a point to the other points. This way, the effects of the "curse of dimensionality" are alleviated compared to purely distance-based approaches. A main advantage of our new approach is that our method does not rely on any parameter selection influencing the quality of the achieved ranking. In a thorough experimental evaluation, we compare ABOD to the well-established distance-based method LOF for various artificial and a real world data set and show ABOD to perform especially well on high-dimensional data.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 AnglebasedOutlierDetectioninHig	Hans-Peter Kriegel Matthias S hubert Arthur Zimek			Angle-based Outlier Detection in High-dimensional Data				10.1145/1401890.1401946