Each observation is treated as its own cluster at the beginning in hierarchical clustering.

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This is true for agglomerative hierarchical clustering, which starts by treating each observation as its own cluster and then iteratively merges the closest clusters until a stopping criterion is met. However, for divisive hierarchical clustering, the opposite is true - all observations are initially grouped into one cluster, which is then recursively subdivided into smaller clusters until a stopping criterion is met.

Each Observation Is Treated as Its Own Cluster at the Beginning