Ward's Method
Updated 2026-08-13
INTRODUCTION
English translation pending.
CORE DEFINITION
Ward's method is an agglomerative hierarchical clustering criterion: at each step it merges the pair of clusters that produces the smallest increase in the total within-cluster sum of squared deviations. The core proposition is that minimizing the variance added at each merge tends to produce compact, evenly sized clusters, which makes the resulting hierarchy interpretable without specifying the number of clusters in advance. The key qualification is that the criterion assumes roughly spherical, similar-sized clusters and depends on the scale of the variables, so standardization is required and non-spherical structures are handled poorly.
SCAFFOLDING EFFECT
Reduce cognitive load
- Scaling first: standardize every variable before clustering, or the largest-magnitude variable dominates. - Dendrogram cut: choose the height at which to cut the tree to obtain a cluster count. - Shape check: confirm the data is roughly spherical before trusting the result.
Anchor fast decisions
Each merge increases the total within-cluster variance by a measurable amount, and choosing the smallest increase keeps clusters tight. Because the criterion penalizes dispersion rather than distance between centroids alone, the resulting groups tend to be compact and comparable in size. This is why the method is popular for exploratory grouping of numeric data, and also why it misbehaves on elongated or nested structures.
MINIMUM ACTION
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Ward%27s_methodverified
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