  Anomaly detection is crucial for understanding unusual behaviors in data, as
anomalies offer valuable insights. This paper introduces Dependency-based
Anomaly Detection (DepAD), a general framework that utilizes variable
dependencies to uncover meaningful anomalies with better interpretability.
DepAD reframes unsupervised anomaly detection as supervised feature selection
and prediction tasks, which allows users to tailor anomaly detection algorithms
to their specific problems and data. We extensively evaluate representative
off-the-shelf techniques for the DepAD framework. Two DepAD algorithms emerge
as all-rounders and superior performers in handling a wide range of datasets
compared to nine state-of-the-art anomaly detection methods. Additionally, we
demonstrate that DepAD algorithms provide new and insightful interpretations
for detected anomalies.
