N-cut (Normalized cut)

The normalized cut (N-cut) is a method used to divide a complex dataset, like a network or image, into meaningful groups. It does this by measuring both how tightly connected items are within each group and how weakly connected different groups are to each other. The goal is to cut the dataset in a way that minimizes the similarities between groups while keeping the members of each group closely related. This approach helps in identifying natural, well-defined clusters or segments, making it useful for tasks like image segmentation, data clustering, and network analysis.