Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as concept drift. Thus, learning models must detect and adapt to such changes, so as to exhibit a good predictive performance after a drift has occurred. In this regard, the development of effective drift detection algorithms becomes a key factor in data stream mining. In this work we propose \(\textit{CURIE}\), a drift detector relying on cellular automata. Specifically, in \(\textit{CURIE}\) the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream. Computer simulations are presented and discussed to show that \(\textit{CURIE}\), when hybridized with other base learners, renders a competitive behavior in terms of detection metrics and classification accuracy. \(\textit{CURIE}\) is compared with well-established drift detectors over synthetic datasets with varying drift characteristics.

数据流挖掘从快速连续流动的大量数据（数据流）中提取信息。它们通常会受到数据分布变化的影响，从而产生一种称为概念漂移的现象。因此，学习模型必须检测并适应这种变化，以便在发生漂移后表现出良好的预测性能。在这方面，开发有效的漂移检测算法成为数据流挖掘的关键因素。在这项工作中，我们提出了\(\textit{CURIE}\)，一种依赖于元胞自动机的漂移检测器。具体来说，在\(\textit{CURIE}\)数据流的分布在元胞自动机的网格中表示，然后可以利用其邻域规则来检测流上可能的分布变化。提出并讨论了计算机模拟，以表明\(\textit{CURIE}\)与其他基础学习器混合时，会在检测指标和分类准确性方面呈现竞争行为。\(\textit{CURIE}\)在具有不同漂移特征的合成数据集上与成熟的漂移检测器进行比较。