Two types:
- Agglomerative hierarchical clustering: Starts with individual nodes, then lumps them together into clusters
- Divisive hierarchical clustering: Starts with whole graph as one cluster, then divides it into smaller clusters
Pros:
- More likely to converge correctly than K-Means Clustering
- Gives you a dendrogram, which gives you a more complete picture of the dataset
Cons:
- High time and space complexity
- Cannot be used for large datasets
- No objective function for hierarchical clustering
- Sensitive to noise and outliers since we use distance metrics
- Difficulty handling large clusters