Sciweavers

36
Voted
PKDD
1999
Springer

OPTICS-OF: Identifying Local Outliers

14 years 4 months ago
OPTICS-OF: Identifying Local Outliers
: For many KDD applications finding the outliers, i.e. the rare events, is more interesting and useful than finding the common cases, e.g. detecting criminal activities in E-commerce. Being an outlier, however, is not just a binary property. Instead, it is a property that applies to a certain degree to each object in a data set, depending on how ‘isolated’ this object is, with respect to the surrounding clustering structure. In this paper, we formally introduce a new notion of outliers which bases outlier detection on the same theoretical foundation as density-based cluster analysis. Our notion of an outlier is ‘local’ in the sense that the outlier-degree of an object is determined by taking into account the clustering structure in a bounded neighborhood of the object. We demonstrate that this notion of an outlier is more appropriate for detecting different types of outliers than previous approaches, and we also present an algorithm for finding them. Furthermore, we show that b...
Markus M. Breunig, Hans-Peter Kriegel, Raymond T.
Added 04 Aug 2010
Updated 04 Aug 2010
Type Conference
Year 1999
Where PKDD
Authors Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jörg Sander
Comments (0)