document: 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 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 .
doc_bio_tags: O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O