## Overview: DISTANCE Procedure

The DISTANCE procedure computes various measures of distance, dissimilarity, or similarity between the observations (rows)
of an input SAS data set, which can contain numeric or character variables, or both, depending on which proximity measure
is used.

The proximity measures are stored as a lower triangular matrix or a square matrix (depending on the SHAPE= option) in an output
data set that can then be used as input to the CLUSTER, MDS, and MODECLUS procedures.

The number of rows and columns in the output data set equals the number of observations in the input data set. If the input
data set contains BY groups, an output matrix is computed for each BY group with the size determined by the maximum number
of observations in any BY group.

The output data set is of type TYPE=DISTANCE or TYPE=SIMILAR, depending on the value of the METHOD= option. See the METHOD=
option for more information about the association between the method and the output data set type.

Data set types do not persist when you copy or modify a data set. You must specify the TYPE= data set option for the new data
set, as in the following example:

data dist2(type=distance);
set dist;
run;

See the OUT=
option for more information about data set type persistence.

PROC DISTANCE also provides various nonparametric and parametric methods for standardizing variables. Different variables
can be standardized with different methods.

Distance matrices are used frequently in data mining, genomics, marketing, financial analysis, management science, education,
chemistry, psychology, biology, and various other fields.