  Anomalies are cases that are in some way unusual and do not appear to fit the
general patterns present in the dataset. Several conceptualizations exist to
distinguish between different types of anomalies. However, these are either too
specific to be generally applicable or so abstract that they neither provide
concrete insight into the nature of anomaly types nor facilitate the functional
evaluation of anomaly detection algorithms. With the recent criticism on 'black
box' algorithms and analytics it has become clear that this is an undesirable
situation. This paper therefore introduces a general typology of anomalies that
offers a clear and tangible definition of the different types of anomalies in
datasets. The typology also facilitates the evaluation of the functional
capabilities of anomaly detection algorithms and as a framework assists in
analyzing the conceptual levels of data, patterns and anomalies. Finally, it
serves as an analytical tool for studying anomaly types from other typologies.
