How Well do Clustering Similarities-Based Concept Drift Detectors Identify Drift in case of Synthetic/Real-World Data?

Concept drift is an unforeseeable change in the underlying data distribution of streaming data, and because of such a change, deployed classifiers over that data show a drop in accuracy. Concept drift detectors are algorithms capable of detecting such a drift, and unsupervised ones detect drift without needing the data’s actual labels, which can...