Dynamic Clustering

Dynamic clustering focuses on adapting clustering algorithms to handle data streams and datasets where cluster structures evolve over time. Current research emphasizes developing efficient algorithms, such as those incorporating self-organizing maps, that can accurately and quickly identify and track changing clusters in high-velocity data streams, even with imbalanced class distributions or sparse data. These advancements are crucial for applications ranging from real-time anomaly detection in healthcare and software engineering to improved traffic prediction and personalized recommendations in recommender systems.