  This paper is based on a previous publication [29]. Our work extends
exception mining and outlier detection to the case of object-relational data.
Object-relational data represent a complex heterogeneous network [12], which
comprises objects of different types, links among these objects, also of
different types, and attributes of these links. This special structure
prohibits a direct vectorial data representation. We follow the
well-established Exceptional Model Mining framework, which leverages machine
learning models for exception mining: A object is exceptional to the extent
that a model learned for the object data differs from a model learned for the
general population. Exceptional objects can be viewed as outliers. We apply
state of-the-art probabilistic modelling techniques for object-relational data
that construct a graphical model (Bayesian network), which compactly represents
probabilistic associations in the data. A new metric, derived from the learned
object-relational model, quantifies the extent to which the individual
association pattern of a potential outlier deviates from that of the whole
population. The metric is based on the likelihood ratio of two parameter
vectors: One that represents the population associations, and another that
represents the individual associations. Our method is validated on synthetic
datasets and on real-world data sets about soccer matches and movies. Compared
to baseline methods, our novel transformed likelihood ratio achieved the best
detection accuracy on all datasets.
