Hierarchical Clustering
Hierarchical clustering builds a nested tree of clusters (dendrogram); the agglomerative approach merges bottom-up while the divisive approach splits top-down.
Purpose
To cluster without a preset cluster count, revealing the hierarchical structure of data.
Mechanism
Agglomerative: start with each point as its own cluster and repeatedly merge the closest pair (using single/complete/average linkage). Cutting the dendrogram at a distance yields different cluster counts. No preset K is needed.
Use cases
- Biological taxonomy
- Document clustering
- Hierarchy discovery