In complete linkage clustering, the distance between two clusters is defined as the maximum distance between any two points in the two clusters. So, the distance between two clusters is determined by the farthest points in each cluster. This approach tends to produce more compact and spherical clusters compared to single linkage clustering.
Complete linkage clustering is less sensitive to noise and outliers compared to single linkage clustering, and it tends to handle uneven cluster sizes better. However, it can be more computationally intensive, especially for large datasets, because it involves calculating the maximum distance between points in each pair of clusters.
Overall, complete linkage hierarchical clustering is a useful method for finding compact and well-separated clusters in data, especially when the clusters have distinct boundaries.
