Overview
Hierarchical clustering creates a multi-level hierarchy of clusters. The results are often visualized using a Dendrogram (a tree-like diagram).
Approaches
- Agglomerative (Bottom-Up): Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
- Divisive (Top-Down): All observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
Advantages
- No need to specify the number of clusters (K) in advance.
- Provides a visual representation of the relationships between data points.