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Complete Linkage: In complete linkage, we define the distance between two clusters to be the maximum distance between any single data point in the first cluster and any single data point in the second cluster.

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.

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.

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.

Complete linkage clustering (farthest neighbor ) is one way to calculate distance between clusters in hierarchical clustering. The method is based on maximum distance; the similarity of any two clusters is the similarity of their most dissimilar pair.

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.

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.