SPSS® 12.0 Command Syntax Reference

PROXIMITIES 1279
RESCALE Rescale the proximity values to a range of 0 to 1. RESCALE standardizes the
proximities by first subtracting the value of the smallest and then dividing by
the range. You would not usually use RESCALE with measures that are
already standardized on meaningful scales, as are correlations, cosines, and
many binary coefficients.
PROXIMITIES can compute any one of a number of measures between items. You can
choose among measures for interval data, frequency count data, or binary data. Available
keywords for each of these types of measures are defined in the following sections.
• Only one measure can be specified. However, each measure can be specified with any of
the transformations ABSOLUTE, REVERSE, or RESCALE. To apply a transformation to an
existing matrix of proximity values without computing any measures, use keyword NONE
(see p. 1287).
• If more than one transformation is specified, PROXIMITIES does them in the order listed
above: first ABSOLUTE, then REVERSE, and then RESCALE regardless of the order they
are specified.
• Each entry in the resulting proximity matrix represents a pair of items. The items can be
either cases or variables, whichever is specified on the VIEW subcommand.
• When the items are cases, the computation for each pair of cases involves pairs of values
for the specified variables.
• When the items are variables, the computation for each pair of variables involves pairs of
values for the variables across all cases.
Example
PROXIMITIES A B C
/MEASURE=EUCLID REVERSE.
• MEASURE specifies a EUCLID measure and a REVERSE transformation.
Measures for Interval Data
To obtain proximities for interval data, use any one of the following keywords on MEASURE:
EUCLID Euclidean distance. The distance between two items, x and y, is the square
root of the sum of the squared differences between the values for the items.
This is the default.
EUCLID( x, y)
Σi( xi – yi) 2
=
SEUCLID Squared Euclidean distance. The distance between two items is the sum of
the squared differences between the values for the items.
SEUCLID( xy , ) Σi( xi – yi) 2
=
CORRELATION Correlation between vectors of values. This is a pattern similarity measure.