Evaluating Subspace Search Methods with Hidden Outlier

Vortragende(r)	Marcel Hiltscher
Vortragstyp	Bachelorarbeit
Betreuer(in)	Georg Steinbuss
Termin	Fr 15. Februar 2019
Vortragssprache
Vortragsmodus
Kurzfassung	In today’s world, most datasets do not have only a small number of attributes. The high number of attributes, which are referred to as dimensions, hinder the search of objects that normally not occur. For instance, consider a money transaction that has been not legally carried out. Such objects are called outlier. A common method to detect outliers in high dimensional datasets are based on the search in subspaces of the dataset. These subspaces have the characteristics to reveal possible outliers. The most common evaluation of algorithms searching for subspaces is based on benchmark datasets. However, the benchmark datasets are often not suitable for the evaluation of these subspace search algorithms. In this context, we present a method that evaluates subspace search algorithms without relying on benchmark datasets by hiding outliers in the result set of a subspace search algorithm.

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