Summary  
This chapter explains different linkage methods for hierarchical clustering, detailing how to choose reference points (e.g., closest, farthest, centroid, or average) to measure distances between clusters and how each choice affects cluster shape.

General domain of usage  
Unsupervised learning (hierarchical clustering)

**Linkage methods** decide how to measure distance between clusters when building clusters step-by-step. Different linkages create **different cluster shapes**.

Which linkage method is known for creating long, chaining clusters and is most sensitive to noise?

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Linkages