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Single link - You link two clusters based on the minimum distance between 2 elements. A drawback of this method is that it tends to produce long thin clusters since you make the link based on only 2 points. Complete link - You link two clusters based on the max distance between 2 elements.

In complete-linkage clustering, the link between two clusters contains all element pairs, and the distance between clusters equals the distance between those two elements (one in each cluster) that are farthest away from each other.

Single linkage is fast, and can perform well on non-globular data, but it performs poorly in the presence of noise. average and complete linkage perform well on cleanly separated globular clusters, but have mixed results otherwise. Ward is the most effective method for noisy data.

Single linkage: computes the minimum distance between clusters before merging them. Complete linkage: computes the maximum distance between clusters before merging them. Average linkage: computes the average distance between clusters before merging them.

Complete-linkage (farthest neighbor) is where distance is measured between the farthest pair of observations in two clusters. This method usually produces tighter clusters than single-linkage, but these tight clusters can end up very close together.

Complete Linkage Clustering vs. Single Linkage Single linkage can result in long stringy clusters and ?chaining? while complete linkage tends to make highly compact clusters [1]. One disadvantage of complete linkage clustering is that it behaves badly when outliers are present [2].

Single Linkage: It is the Shortest Distance between the closest points of the clusters. Consider the below image: Complete Linkage: It is the farthest distance between the two points of two different clusters. It is one of the popular linkage methods as it forms tighter clusters than single-linkage.