Hierarchical Clustering
Bottom-up clustering that builds a hierarchy of clusters
What is Hierarchical Clustering?
Hierarchical clustering creates a tree of clusters called a dendrogram. It can be either agglomerative (bottom-up) or divisive (top-down). The agglomerative approach starts with each point as its own cluster and merges the closest pairs iteratively.
Linkage Criteria:
- Ward: Minimizes within-cluster variance
- Complete: Maximum distance between clusters
- Average: Average distance between all pairs
- Single: Minimum distance between clusters
Advantages:
- No need to specify K in advance
- Dendrogram provides complete hierarchy
- Can find clusters at multiple scales
- Works with any distance metric
- Deterministic (no random initialization)
Parameters
Distance calculation method
Fewer samples for clearer dendrogram
Cluster Visualization
Results
Run clustering to see results
Understanding Hierarchical Clustering
When to Use
- Need to explore different granularities
- Hierarchical structure in data
- Small to medium datasets
- Unknown optimal number of clusters
- Need deterministic results
- Taxonomies and phylogenetic trees
Limitations
- O(n³) time complexity
- O(n²) space complexity
- Cannot undo merges (greedy)
- Sensitive to noise and outliers
- Not suitable for large datasets
- Difficult to interpret for many clusters
Linkage Methods Comparison
Ward: Best for spherical, similarly-sized clusters
Complete: Compact clusters, sensitive to outliers
Average: Balanced approach, less sensitive to outliers
Single: Can handle elongated clusters, prone to chaining