  The world surrounding us is subject to constant change. These changes,
frequently described as concept drift, influence many industrial and technical
processes. As they can lead to malfunctions and other anomalous behavior, which
may be safety-critical in many scenarios, detecting and analyzing concept drift
is crucial. In this paper, we provide a literature review focusing on concept
drift in unsupervised data streams. While many surveys focus on supervised data
streams, so far, there is no work reviewing the unsupervised setting. However,
this setting is of particular relevance for monitoring and anomaly detection
which are directly applicable to many tasks and challenges in engineering. This
survey provides a taxonomy of existing work on drift detection. Besides, it
covers the current state of research on drift localization in a systematic way.
In addition to providing a systematic literature review, this work provides
precise mathematical definitions of the considered problems and contains
standardized experiments on parametric artificial datasets allowing for a
direct comparison of different strategies for detection and localization.
Thereby, the suitability of different schemes can be analyzed systematically
and guidelines for their usage in real-world scenarios can be provided.
Finally, there is a section on the emerging topic of explaining concept drift.
