LAUSR

Abstract In the past few decades, research related to concept drift learning has been increasing, and many concept drift learning algorithms have also been developed and applied to actual data stream processing. In general, concept drift research involves the development of methodologies and techniques for drift detection, understanding and adaptation. This paper focuses on concept drift detection, and proposes an unsupervised concept drift detection algorithm based on multi-scale slide windows, where the total average distance is obtained through k-means clustering and multi-scale windows and is used as a detection index for concept drift, and then uses the statistical process control system to determine the range of index thresholds. Proved by experiments of detecting the gradual and abrupt concept drift with five datasets of different dimensions, including Sin, Circle, Gaussian, Radar and Motion Sense datasets, the algorithm has a good concept drift detection effect.