Angle-based outlier detection in high-dimensional data

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 co...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: Hans-Peter Kriegel, Matthias Schubert, Arthur Zimek
• Keywords: Data Mining, Data Mining Task, Euclidean Data Space, KDD 2008, Well-established Distance-based Method