Describe a scenario where Hierarchical Clustering would be more beneficial than K-Means Clustering, and explain the considerations in choosing the linkage method.
- When a fixed number of clusters is required
- When clusters are uniformly distributed
- When clusters have varying sizes and non-spherical shapes
- When computational efficiency is the priority
Hierarchical Clustering is more beneficial than K-Means when clusters have varying sizes and non-spherical shapes. Unlike K-Means, Hierarchical Clustering does not assume spherical clusters and can handle complex structures. The choice of linkage method will depend on the specific characteristics of the clusters, with considerations like distance metric and desired cluster shape guiding the selection.
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