User Guide

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Hierarchical C
luster Analysis
Hierarchica
l Cluster Analysis Method
Figure 32-3
Hierarchical Cluster Analysis Method dialog box
Cluster Method. Available alternatives are between-groups linkage, within-groups
linkage, nearest neighbor, furthest neighbor, centroid clustering, median clustering,
and Ward’s method.
Measure. Allows you to specify the distance or similarity measure to be used in
clustering. Select the type of data and the appropriate distance or similarity measure:
 Interval data. Available alternatives are Euclidean distance, squared Euclidean
distance, cosine, Pearson correlation,Chebychev,block,Minkowski,and
customized.
 Count data. Available alternatives are chi-square measure and phi-square measure.
 Binary data. Available alternatives are Euclidean distance, squared Euclidean
distance, size difference, pattern difference, variance, dispersion, shape, simple
matching, phi 4-point correlation, lambda, Anderberg’s D, dice, Hamann,
Jaccard, Kulczynski 1, Kulczynski 2, Lance and Williams, Ochiai, Rogers and
Tanimoto, Russel and Rao, Sokal and Sneath 1, Sokal and Sneath 2, Sokal and
Sneath 3, Sokal and Sneath 4, Sokal and Sneath 5, Yule’s Y,andYule’sQ.