Normalized Cuts

Normalized Cuts is a method used in image processing and data clustering to segment a set of objects or pixels into meaningful groups. It works by evaluating how well connected the items within each group are compared to how connected they are to others, aiming to divide the data into parts that are internally similar but distinct from each other. This approach balances the goal of minimizing the similarity between different groups while maintaining the overall cohesion within each group, leading to more accurate and natural segmentations or classifications in complex datasets.