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.

在过去的几十年中，与概念漂移学习相关的研究不断增加，并且许多概念漂移学习算法也已开发并应用于实际数据流处理。通常，概念漂移研究涉及漂移检测，理解和适应的方法和技术的发展。本文以概念漂移检测为重点，提出了一种基于多尺度滑动窗口的无监督概念漂移检测算法，该算法通过k均值聚类和多尺度窗口获得总平均距离，并将其作为概念的检测指标。漂移，然后使用统计过程控制系统确定指标阈值的范围。通过使用五个不同维度的数据集检测渐进和突变概念漂移的实验证明，