Global Clustering

Global clustering aims to partition large, distributed datasets into meaningful groups, addressing challenges like data heterogeneity, privacy concerns, and incomplete information. Current research focuses on developing federated learning approaches, often employing graph neural networks or spectral clustering methods, to collaboratively build global cluster models from decentralized data sources while preserving privacy. These advancements are significant for improving scalability and efficiency in various applications, including name disambiguation, domain adaptation, and large-scale time series forecasting, where centralized approaches are impractical or infeasible. The resulting global clustering solutions offer improved accuracy and efficiency compared to traditional methods.