Research on Stream Data Mining focuses on developing techniques to efficiently analyze and extract insights from high-velocity, real-time data streams. Recent advancements include sliding window-based methods for rare pattern mining, gradient-boosted bagging ensembles for improved regression performance in evolving streams, and scalable concept drift adaptation frameworks that balance predictive accuracy with memory and computational efficiency. Algorithms like Dynamic Fast Decision Tree (DFDT) optimize predictive modeling for IoT edge devices, while approaches such as SiameseDuo++ leverage active learning to handle limited labeled data and concept drift. Overall, these innovations enhance the ability to process, model, and interpret continuous data streams for timely and accurate decision-making in dynamic environments.