Agglomerative Clustering: Single vs Complete Linkage, Step by Step
This lesson is part of the course
In this lesson
You run hierarchical clustering by hand on six pilot stores: find the closest pair, merge it, update the distances, and see how single and complete linkage build different trees.
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Lessons in this course
- Distance Matrices: Euclidean, Manhattan & Why Scaling Comes First Locked
- Agglomerative Clustering: Single vs Complete Linkage, Step by Step Locked
- Reading a Dendrogram: Merge Heights, the Largest Gap & Cluster Labels Locked
- Silhouette Score per Store: a, b, s and the Average Locked
- Choosing k: Elbow Rule vs Silhouette Locked
- k-Medoids (PAM): Total Cost and One Swap Step Locked
- DBSCAN by Hand: eps, minPts and Core, Border & Noise Points Locked
- Cluster Profiling and a Scatter Chart Coloured by Cluster Locked