Interactive Linkage Group Clustering Method

Use the drop-down menu to specify the method to use for joining clusters to form linkage groups via hierarchical clustering. Different algorithms might work better than others depending on your data.

Note: This parameter is available only when Interactive Hierarchical Clustering has been selected from the Choose a linkage grouping method field.

Clustering methods are described in the following table:

Clustering Method	Description
Average	Choose this method to defines clusters based on the average distance (recombination rate) between pairs of markers from different clusters. This method is specified by default.
Centroid	Choose this method to set the distance between clusters to the squared Euclidean Distance between the means of each cluster.1 This method is more robust than other clustering methods.
Ward	Choose this method to set the distance between clusters to the ANOVA sum of squares across all markers between clusters. At each generation, two clusters from the previous generation are merged to reduce the within-cluster sum of squares over all partitions. The sums of squares are easier to interpret when they are divided by the total sum of squares to give the proportions of variance (squared semipartial correlations). This method joins clusters to maximize the likelihood at each level of the hierarchy under the assumptions of multivariate normal mixtures, spherical covariance matrices, and equal sampling probabilities. This method tends to join clusters with a small number of observations and is biased toward producing clusters with approximately the same number of observations. It is also very sensitive to outliers.2
Single	Choose this method to define the distance between clusters based on the minimum recombination rate between a marker in one cluster and a marker in a different cluster. Because there are no constraints on the shape of clusters, single linkage sacrifices performance in the recovery of compact clusters in return for the ability to detect elongated and irregular clusters. Single linkage tends to chop off the tails of distributions before separating the main clusters.
Complete	Choose this method to define the distance between clusters based on the maximum recombination rate between a marker in one cluster and a marker in a different cluster. This method is biased toward producing clusters of equivalent diameters and can be distorted by even moderate outliers.

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1

Milligan, G.W. (1980) An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika 45: 325-342.

To Specify a Clustering Method:

Specify Interactive Hierarchical Clustering in the Choose a linkage grouping method field.

Make a selection using the drop-down menu.

See the JMP documentation on the hierarchical clustering platform for more information.