Some data doesn't just cluster; it evolves. Hierarchical clustering reveals the nested relationships within your dataset through the power of tree structures.
1Bottom-Up Merges
Agglomerative Clustering is the most common approach. It starts with every single data point as a cluster of one. It then iteratively finds the two closest clusters and merges them into a larger group. This continues until only one giant cluster remains.
2Visualizing the Tree
The Dendrogram is a specialized plot that shows every single merge operation. By looking at the heights of the horizontal lines, you can tell how dissimilar clusters were before merging. Longer vertical lines indicate more distinct separation between groups.
3Linkage Methods
How do we measure the distance between clusters? Linkage defines the rule. Ward's method minimizes the variance of merged clusters, while Single linkage uses the distance between the two closest points. Choosing the right linkage is critical for accurate clustering.
