Ward

Ward's method is a way of grouping data points or objects based on their similarities. Imagine organizing items into clusters so that those in the same group are more similar to each other than to those in other groups. The process starts with each item as its own group and then combines the most similar pairs step-by-step. The goal is to minimize the total within-group differences, which helps reveal natural groupings or patterns in the data. Ward's method is commonly used in hierarchical clustering to produce meaningful, compact clusters.