A data scientist applies hierarchical clustering using PROC CLUSTER with METHOD=WARD. What does Ward's linkage criterion minimize at each merge step?
Select an answer to reveal the explanation.
Short Explanation
You're right to pick the criterion that minimizes the increase in total within-cluster sum of squares when two clusters merge. That's Ward's linkage, and it tends to produce compact, similarly sized clusters.
Full Explanation
Ward's linkage minimizes the increase in total within-cluster sum of squares (WCSS) caused by merging two clusters, producing compact, similarly sized clusters. The maximum-distance criterion describes complete linkage, because it uses the farthest pair of points. The average-distance criterion describes average linkage, such as UPGMA. The centroid-distance criterion describes centroid linkage.