Unknown Number

Research on handling an unknown number of clusters or sources is a significant challenge across diverse fields, aiming to automate the identification of groupings within data without prior knowledge of their number. Current efforts focus on developing algorithms and neural network architectures, such as those employing community detection, recursive extraction, and attractor-based methods, that dynamically adapt to the number of clusters discovered during processing. These advancements improve the efficiency and accuracy of tasks ranging from single-cell analysis and image fusion to speech separation and sound source localization, impacting fields from biology and computer vision to audio processing and machine learning.