hierarchical clustering

Description: Hierarchical clustering builds a hierarchy of clusters using either a bottom-up (agglomerative) or top-down (divisive) approach.

Key Points:

• Does not require a predefined number of clusters.
• Produces a dendrogram for visualizing the hierarchy.
• Computationally intensive for large datasets.

Applications: Social network analysis, gene sequence analysis, document clustering.