When performing hierarchical clustering, which linkage method considers the maximum distance between points in two clusters?

Question

BlackTom AI · Accepted Answer

When evaluating how hierarchical clustering decides which clusters to merge next, it's important to match the linkage rule to the distance measure used between clusters.
Option 1: Single Linkage. This method defines the inter-cluster distance as the minimum distance between any pair of points from the two clusters. It tends to create elongated, chain-like clusters and is sensitive to outliers. This is not the same as using the maximum pairwise distance.
Option 2: Complete Linkage. This approach defines the inter-cluster distance as the maximum distance between any pair of points, one from each cluster. It tends to produce more compact and spherical clusters because the worst-case (largest) separation governs the merge decision, which aligns with the description in the question.
Option 3: Average Linkage. Here, the distance between clusters is the average of all pairwise distances between points in the two clusters. While it smooths out extremes, it does not focus on the maximum distance, so the question’s criterion is not about the average case.
Option 4: Ward's Method. Ward's criterion merges clusters based on the increasing of the within-cluster sum of squares, effectively aiming to minimize total variance; it is not defined by a simple maximum between-point distance and thus does not match the stated criterion.
In summary, the statement matches Complete Linkage, which uses the maximum pairwise distance between points across the two clusters to determine mergers, unlike the other linkage strategies described above.

When performing hierarchical clustering, which linkage method considers the maximum distance between points in two clusters?Single choice

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