Joint Cluster

Joint cluster analysis focuses on simultaneously performing clustering and another task, such as classification, embedding, or trajectory planning, leveraging the inherent relationships between data points and their cluster assignments. Current research employs diverse approaches, including graph neural networks, transformers, and reinforcement learning, often incorporating techniques like contrastive learning and hierarchical clustering to improve efficiency and accuracy. This integrated approach enhances model performance in various domains, from improving the robustness of graph neural networks for node classification to optimizing data sharing in federated learning and enabling more accurate and interpretable analysis of complex datasets like brain networks and biomedical data for precision medicine.