Concept Drift Detection with Clustering via Statistical Change Detection Methods
Yusuke Sakamoto, Ken–ichi Fukui, João Manuel Portela da Gama, Daniela Nicklas, Koichi Moriyama, Masayuki Numao · 2015
We propose a concept drift detection method utilizing statistical change detection in which a drift detection method and the Page-Hinkley test are employed. Our method enables users to annotate clustering results without constructing a model of drift detection for every input. In our experiments using synthetic data, we evaluated our proposed method on the basis of detection delay and false detection, also revealed relations between the degree of drift and parameters of the method.