Hierarchical Cluster Analysis Method
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. Available alternatives are Euclidean distance, squared Euclidean distance, cosine, Pearson correlation, Chebychev, block, Minkowski, and customized.
- Counts. Available alternatives are chi-square measure and phi-square measure.
- Binary. 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, and Yule's Q.
Transform Values. Allows you to standardize data values for either cases or values before computing proximities (not available for binary data). Available standardization methods are z scores, range −1 to 1, range 0 to 1, maximum magnitude of 1, mean of 1, and standard deviation of 1.
Transform Measures. Allows you to transform the values generated by the distance measure. They are applied after the distance measure has been computed. Available alternatives are absolute values, change sign, and rescale to 0–1 range.
Specifying the Clustering Method
This feature requires the Statistics Base option.
- From the menus choose:
- In the Hierarchical Cluster Analysis dialog box, click Method.