Parameters | Genetics | 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.

Choose this method to set the distance between clusters to the squared Euclidean Distance between the means of each cluster.^{1} 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

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