Output Data Sets
The OUT= data set contains one complete copy of the input data set
for each cluster solution. There are additional variables
identifying each solution and giving information about individual
observations. Solutions with only one remaining cluster when JOIN=p
is specified are omitted from the OUT= data set
(see the description of the
JOIN= option).
The OUT= data set can be extremely large, so it may
be advisable to specify the DROP= data set option to exclude
unnecessary variables.
The OUTCLUS= or OUTC= data set
contains one observation for each cluster
in each cluster solution. The variables identify the solution
and provide statistics describing the cluster.
The OUTSUM= or OUTS= data set
contains one observation for each cluster solution.
The variables identify the solution and provide information
about the solution as a whole.
The following variables can appear in all of the output data sets:
- _K_, which is the value of the K= option for the current solution.
This variable appears only if you specify the K= option.
- _DK_, which is the value of the DK= option for the current solution.
This variable appears only if you specify the DK= option.
- _CK_, which is the value of the CK= option for the current solution.
This variable appears only if you specify the CK= option.
- _R_, which is the value of the R= option for the current solution.
This variable appears only if you specify the R= option.
- _DR_, which is the value of the DR= option for the current solution.
This variable appears only if you specify the DR= option.
- _CR_, which is the value of the CR= option for the current solution.
This variable appears only if you specify the CR= option.
- _CASCAD_, which is the number of times the density estimates have
been cascaded for the current solution.
This variable appears only if you specify the CASCADE= option.
- _METHOD_, which is the value of the METHOD= option for the current
solution. This variable appears only if you specify the METHOD= option.
- _NJOIN_, which is the number of clusters that are joined or
dissolved in the current solution.
This variable appears only if you specify the JOIN option.
- _LOCAL_, which is the local dimensionality estimate of the
observation.
This variable appears only if you specify the LOCAL option.
The OUT= data set contains the following variables:
- the variables from the input data set
- _OBS_, which is the observation number from the input data set.
This variable appears only if you omit the ID statement.
- DENSITY, which is the estimated density at the observation.
This variable can be renamed by the DENSITY= option.
- CLUSTER, which is the number of the cluster to which
the observation is assigned.
This variable can be renamed by the CLUSTER= option.
The OUTC= data set contains the following variables:
- the BY variables, if any
- _NCLUS_, which is the number of clusters in the solution
- CLUSTER, which is the number of the current cluster
- _FREQ_, which is the number of observations in the cluster
- _MODE_, which is the maximum estimated density in the cluster
- _BFREQ_, which is the number of observations in the cluster with
neighbors belonging to a different cluster
- _SADDLE_, which is the estimated saddle density for the cluster
- _MC_, which is the number of observations within the fixed-radius
density-estimation neighborhood of the modal observation.
This variable appears only if you specify the TEST or JOIN option.
- _SC_, which is the number of observations within the fixed-radius
density-estimation neighborhood of the saddle observation.
This variable appears only if you specify the TEST or JOIN option.
- _OC_, which is the number of observations within the overlap of the
two previous neighborhoods.
This variable appears only if you specify the TEST or JOIN option.
- _Z_, which is the approximate z statistic for the cluster.
This variable appears only if you specify the TEST or JOIN option.
- _P_, which is the approximate p-value for the cluster.
This variable appears only if you specify the TEST or JOIN option.
The OUTS= data set contains the following variables:
- the BY variables, if any
- _NCLUS_, which is the number of clusters in the solution
- _UNCL_, which is the number of unclassified observations
- _CROSS_, which is the likelihood cross-validation
criterion if you specify the
CROSS or CROSSLIST option
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
