Learning Contrastive Evolving Micro-Clusters for Robust Semi-Supervised Data Stream Classification

Semi-supervised learning on data streams with concept drift has attracted considerable attention in recent decades. Many existing algorithms have achieved promising results on low-dimensional data streams by leveraging unlabeled data and adapting to nonstationary distributions. However, real-world data streams often exhibit complex entanglement and high dimensionality, which are usually overlooked by current approaches, resulting in fragile classification performance. This highlights the need for effective representation learning on evolving data streams to enhance the reliability of model prediction. To this end, we propose a novel algorithm for online semi-supervised learning on high-dimensional data streams by learning from contrastive evolving micro-clusters (MCs), named CEMC. Unlike existing methods, CEMC explicitly mitigates feature entanglement through contrastive MC representation learning, with model initialization guided by contrastive objectives and representation updates triggered by potential drift. To ensure reliable semi-supervised learning, we further model the reliability of contrastive MCs to support online classification and enable rapid adaptation to concept drift. By maintaining contrastive MCs online, CEMC preserves and adapts to evolving concepts within a limited memory budget, while sustaining a discriminative representation space. Empirical results on fourteen real-world benchmark datasets demonstrate the effectiveness of CEMC compared to six state-of-the-art algorithms.