  Analyzing sequence data usually leads to the discovery of interesting
patterns and then anomaly detection. In recent years, numerous frameworks and
methods have been proposed to discover interesting patterns in sequence data as
well as detect anomalous behavior. However, existing algorithms mainly focus on
frequency-driven analytic, and they are challenging to be applied in real-world
settings. In this work, we present a new anomaly detection framework called
DUOS that enables Discovery of Utility-aware Outlier Sequential rules from a
set of sequences. In this pattern-based anomaly detection algorithm, we
incorporate both the anomalousness and utility of a group, and then introduce
the concept of utility-aware outlier sequential rule (UOSR). We show that this
is a more meaningful way for detecting anomalies. Besides, we propose some
efficient pruning strategies w.r.t. upper bounds for mining UOSR, as well as
the outlier detection. An extensive experimental study conducted on several
real-world datasets shows that the proposed DUOS algorithm has a better
effectiveness and efficiency. Finally, DUOS outperforms the baseline algorithm
and has a suitable scalability.
