Python for Machine Learning - The Complete Beginners Course - Hierarchical Clustering

Hierarchical clustering is a method of creating clusters with a predetermined order from top to bottom. It is similar to how files and folders are organized on a hard disk. This clustering method allows us to see the relationships between subclusters. There are two main strategies: divisive, which is a top-down approach starting with a large cluster and breaking it into smaller ones, and agglomerative, which is a bottom-up approach where each observation starts in its own cluster and clusters are merged as they move up the hierarchy.

Describe the divisive strategy of hierarchical clustering.

How does agglomerative clustering differ from divisive clustering?