In data analysis, anomaly detection (also referred to as outlier
detection and sometimes as novelty detection) is generally understood to
be the identification of rare items, events or observations which deviate
significantly from the majority of the data and do not conform to a well
defined notion of normal behavior. Such examples may arouse suspicions of
being generated by a different mechanism, or appear inconsistent with the
remainder of that set of data.
Anomaly detection finds application in many domains including cybersecurity,
medicine, machine vision, statistics, neuroscience, law enforcement and
financial fraud to name only a few.