Outlier detection and ranking based on subspace clustering

Abstract

Detecting outliers is an important task for many applications
including fraud detection or consistency validation in real world
data. Particularly in the presence of uncertain data or imprecise data,
similar objects regularly deviate in their attribute values. The notion
of outliers has thus to be defined carefully. When considering outlier
detection as a task which is complementary to clustering, binary decisions
whether an object is regarded to be an outlier or not seem to be
near at hand. For high-dimensional data, however, objects may belong
to different clusters in different subspaces. More fine-grained concepts to
define outliers are therefore demanded. By our new OutRank approach,
we address outlier detection in heterogeneous high dimensional data and
propose a novel scoring function that provides a consistent model for
ranking outliers in the presence of different attribute types. Preliminary
experiments demonstrate the potential for successful detection and reasonable ranking of outliers in high dimensional data sets.