Normalized Cuts is a graph-based clustering method used in image segmentation and data analysis. It aims to partition a graph into distinct groups while minimizing the dissimilarity between the groups and maximizing the similarity within each group. This is achieved by evaluating the cuts in the graph, which represent the connections between different clusters. The technique works by defining a cost function that balances the size of the cuts with the total connections within the clusters. By normalizing the cut cost, Normalized Cuts ensures that larger clusters do not dominate the clustering process, leading to more meaningful and balanced groupings.