  Most of existing outlier detection methods assume that the outlier factors
(i.e., outlierness scoring measures) of data entities (e.g., feature values and
data objects) are Independent and Identically Distributed (IID). This
assumption does not hold in real-world applications where the outlierness of
different entities is dependent on each other and/or taken from different
probability distributions (non-IID). This may lead to the failure of detecting
important outliers that are too subtle to be identified without considering the
non-IID nature. The issue is even intensified in more challenging contexts,
e.g., high-dimensional data with many noisy features. This work introduces a
novel outlier detection framework and its two instances to identify outliers in
categorical data by capturing non-IID outlier factors. Our approach first
defines and incorporates distribution-sensitive outlier factors and their
interdependence into a value-value graph-based representation. It then models
an outlierness propagation process in the value graph to learn the outlierness
of feature values. The learned value outlierness allows for either direct
outlier detection or outlying feature selection. The graph representation and
mining approach is employed here to well capture the rich non-IID
characteristics. Our empirical results on 15 real-world data sets with
different levels of data complexities show that (i) the proposed outlier
detection methods significantly outperform five state-of-the-art methods at the
95%/99% confidence level, achieving 10%-28% AUC improvement on the 10 most
complex data sets; and (ii) the proposed feature selection methods
significantly outperform three competing methods in enabling subsequent outlier
detection of two different existing detectors.
