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

One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments