Annual Report 2010 - Fachgruppe Informatik an der RWTH Aachen ...

Outlier Ranking in High Dimensional Data
Emmanuel Müller
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 or
imprecise data, similar objects regularly deviate in their attribute values. Thus, the notion of
outliers has 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. However, for high dimensional data objects may belong to
different clusters in different subspaces. More fine-grained concepts to define outliers are
therefore demanded. By our new outlier ranking approaches, we address outlier detection in
subspaces of high dimensional data. We propose novel scoring functions that provide
consistent models for ranking outliers in the presence of object deviation in arbitrary subspace
projections.
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Figure 7: Outliers hidden in arbitrary subsets of the attributes
Clustering in Attributed Graphs
Stephan Günnemann, Brigitte Boden
The aim of data mining approaches is to extract novel knowledge from large sets of data.
These data can be represented in different manners: high-dimensional attribute data to
characterize single objects and graph data to represent the relations between objects. While
the first data type is analyzed by subspace clustering approaches, the second one is analyzed
by dense subgraph clustering methods. For many applications both types of data (attributes
and relationships) are available and can be modeled as graphs with attributed nodes.
Analyzing both data sources simultaneously can increase the quality of mining methods.
However, most clustering approaches deal only with one of these data types. In our works, we
develop novel methods that use both data types simultaneously and thereby obtain better
clustering results.
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