Clustering Framework

Clustering frameworks aim to group similar data points into clusters, facilitating data analysis and interpretation across diverse applications. Recent research emphasizes developing efficient and robust clustering methods for large-scale datasets, including distributed and federated learning settings, often incorporating neural networks (e.g., using self-organized neural implicit surfaces or Vision Transformers) and novel algorithms like those based on distributional kernels or density peak variations. These advancements improve clustering accuracy and scalability, impacting fields ranging from image segmentation and natural language processing to machine learning model training and optimization.