Data Clustering

Data clustering aims to partition data points into groups (clusters) based on similarity, facilitating data analysis and interpretation. Current research emphasizes developing robust and efficient algorithms for diverse data types, including high-dimensional, categorical, and graph data, with a focus on improving scalability and incorporating fairness considerations. Prominent approaches involve density-based methods, Bayesian techniques, and deep learning models, often incorporating graph structures or self-supervised learning. These advancements are impacting various fields, from satellite communication optimization and medical image analysis to financial modeling and news prioritization.