Why does the 'ward' linkage method in hierarchical clustering minimize the total within-cluster variance?

hard📝 Conceptual Q10 of 15

SciPy - Clustering and Distance

ABecause it averages distances between all points in clusters

BBecause it merges clusters to minimize the increase in total squared Euclidean distance

CBecause it uses the minimum distance between points in clusters

DBecause it merges clusters based on maximum distance between points

Step-by-Step Solution

Solution:

Step 1: Understand ward linkage principle
Ward linkage merges clusters to minimize increase in total within-cluster variance.
Step 2: Connect variance to squared Euclidean distance
This is equivalent to minimizing increase in total squared Euclidean distance when merging.
Final Answer:
Because it merges clusters to minimize the increase in total squared Euclidean distance -> Option B
Quick Check:
Ward linkage minimizes variance via squared distances [OK]

Quick Trick: Ward linkage minimizes variance by squared distance [OK]

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