User Guide

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Chapter 25
Distances Si
milarity Measures
Figure 25-3
Distances Similarit y Measures dialog box
From the Measure group, select the alternative that corresponds to your type of data
(interval or binary); then, from the drop-down list, select one of the measures that
corresponds to that type of data. Available measures, by data type, are:
 Interval data. Pearson correlation or cosine.
 Binary d ata. Russell and Rao, simple matching, Jaccard, Dice, Rogers and
Tanimoto,SokalandSneath1,SokalandSneath2,SokalandSneath3,
Kulczynski 1, Kulczynski 2, Sokal and Sneath 4, Hamann, Lambda, Anderberg’s
D,Yule’sY,Yule’sQ, Ochiai, Sokal and Sneath 5, phi 4-point correlation, or
dispersion. (Enter values for Present and Absent to specify which two values are
meaningful; Distances will ignore all other values.)
The Transform Values group allows you to standardize data values for either cases or
variables before computing proximities. These transformations are not applicable to
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.
The Transform Measures group allows you to transform the values generated by
the distance measure. They are applied after the distance measure has been computed.
Available options are absolute values, change sign, and rescale to 0–1 range.