  This study explores the concept of high-density anomalies. As opposed to the
traditional concept of anomalies as isolated occurrences, high-density
anomalies are deviant cases positioned in the most normal regions of the data
space. Such anomalies are relevant for various practical use cases, such as
misbehavior detection and data quality analysis. Effective methods for
identifying them are particularly important when analyzing very large or noisy
sets, for which traditional anomaly detection algorithms will return many false
positives. In order to be able to identify high-density anomalies, this study
introduces several non-parametric algorithmic frameworks for unsupervised
detection. These frameworks are able to leverage existing underlying anomaly
detection algorithms and offer different solutions for the balancing problem
inherent in this detection task. The frameworks are evaluated with both
synthetic and real-world datasets, and are compared with existing baseline
algorithms for detecting traditional anomalies. The Iterative Partial Push
(IPP) framework proves to yield the best detection results.
