Discriminative Clustering

Discriminative clustering aims to discover meaningful data groupings that are not only distinct but also informative, often focusing on maximizing the separation between clusters. Recent research emphasizes developing deep learning models, leveraging techniques like variational EM and generalized mutual information (GEMINI), to achieve this goal, often incorporating feature selection or disentanglement for improved performance. These advancements are improving the accuracy and efficiency of clustering in diverse applications, including network analysis, domain adaptation, and federated learning, where identifying distinct and informative clusters is crucial.