Summary  
This chapter explains various linkage methods for hierarchical clustering, detailing how to compute distances between clusters using single, complete, average, centroid, and Ward linkages.

General domain of usage  
Machine learning

**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?

Explore the power of hidden patterns with unsupervised learning. Master the most influential clustering algorithms, including K-Means, Hierarchical Clustering, DBSCAN, and Gaussian Mixture Models. Learn to evaluate cluster quality using WSS and Silhouette scores, handle diverse distance measures, and implement robust solutions on real-world datasets. Build the skills to segment customers and discover structures in unlabeled data using Scikit-learn.

Linkages