Table 2.

Cluster Distance Measures.

Linkage Type	Process	Advantage	Disadvantage
Single-linkage or nearest neighbor	Combines two clusters together that have the smallest amount of dissimilarity (distance) between the closest pair of data points belonging to the different clusters.	Less sensitive to outliers	Based completely on single links between individual data points and cluster, forms elongated chains
Complete-linkage or furthest-neighbor	Combines two clusters together that have the largest amount of dissimilarity (distance) between the farthest pair of data points belonging to the different clusters.	Compact, hyperspherical clusters composed of very similar data points	Vulnerable to outliers Tends to break large clusters apart All clusters have the same diameter, so smaller clusters are merged with larger ones
Average-linkage or minimum variance	Combines two clusters together after an average distance measure for all preexisting data points belonging to the different cluster is calculated. Clusters are combined together only if a predetermined mathematical threshold is obtained.	Less sensitive to outliers	Tendency to split elongated clusters in half and tail portions of clusters tend to merge with neighboring clusters
Ward's method	Minimizes intracluster variance via calculation of the error sum of squares. Combinations are made that yield the smallest error sum of squares.	Clusters are relatively equal in size and shape	Cannot be used with binary variables Has a tendency to form globular clusters