Affinity propagation

Affinity propagation is a clustering algorithm that automatically finds groups within data by identifying representative examples called "exemplars." It works by exchanging messages between data points to determine how well each point fits into a potential cluster, considering similarity measures. Unlike traditional methods that require pre-setting the number of clusters, affinity propagation dynamically determines them based on the data’s natural structure. It’s useful for organizing complex data into meaningful categories without manual input on the number of groups, making it a powerful tool for pattern recognition, image analysis, and other data-driven tasks.