Output Data Sets

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The FASTCLUS Procedure

Output Data Sets

OUT= Data Set

The OUT= data set contains

the original variables
a new variable taking values from 1 to the value specified in the MAXCLUSTERS= option, indicating the cluster to which each observation has been assigned. You can specify the variable name with the CLUSTER= option; the default name is CLUSTER.
a new variable, DISTANCE, giving the distance from the observation to its cluster seed

If you specify the IMPUTE option, the OUT= data set also contains a new variable, _IMPUTE_, giving the number of imputed values in each observation.

OUTSEED= Data Set

The OUTSEED= data set contains one observation for each cluster. The variables are as follows:

the BY variables, if any
a new variable giving the cluster number. You can specify the variable name with the CLUSTER= option. The default name is CLUSTER.
either the FREQ variable or a new variable called _FREQ_ giving the number of observations in the cluster
the WEIGHT variable, if any
a new variable, _RMSSTD_, giving the root mean square standard deviation for the cluster. See Chapter 23, "The CLUSTER Procedure," for details.
a new variable, _RADIUS_, giving the maximum distance between any observation in the cluster and the cluster seed
a new variable, _GAP_, containing the distance between the current cluster mean and the nearest other cluster mean. The value is the centroid distance given in the output.
a new variable, _NEAR_, specifying the cluster number of the nearest cluster
the VAR variables giving the cluster means

If you specify the LEAST=p option with a value other than 2, the _RMSSTD_ variable is replaced by the _SCALE_ variable, which contains the pooled scale estimate analogous to the root mean square standard deviation but based on pth power deviations instead of squared deviations:

LEAST=1: mean absolute deviation
LEAST=p: root mean pth-power absolute deviation
LEAST=MAX: maximum absolute deviation

If you specify the OUTITER option, there is one set of observations in the OUTSEED= data set for each pass through the data set (that is, one set for initial seeds, one for each iteration, and one for the final clusters). Also, several additional variables appear:

_ITER_: is the iteration number. For the initial seeds, the value is 0. For the final cluster means or centers, the _ITER_ variable is one greater than the last iteration reported in the iteration history.
_CRIT_: is the clustering criterion as described under the LEAST= option.
_CHANGE_: is the maximum over clusters of the relative change in the cluster seed from the previous iteration. The relative change in a cluster seed is the distance between the old seed and the new seed divided by a scaling factor. If you do not specify the LEAST= option, the scaling factor is the minimum distance between the initial seeds. If you specify the LEAST= option, the scaling factor is an L₁ scale estimate and is recomputed on each iteration.
_HOMPAR_: is the value of the homotopy parameter. This variable appears only for LEAST=p with 1<p<2.
_BINSIZ_: is the maximum bin size used for estimating medians. This variable appears only for LEAST=1.

If you specify the OUTITER option, the variables _SCALE_ or _RMSSTD_, _RADIUS_, _NEAR_, and _GAP_ have missing values except for the last pass.

You can use the OUTSEED= data set as a SEED= input data set for a subsequent analysis.

OUTSTAT= Data Set

The variables in the OUTSTAT= data set are as follows:

BY variables, if any
a new character variable, _TYPE_, specifying the type of statistic given by other variables (see Table 27.2 and Table 27.3)
a new numeric variable giving the cluster number. You can specify the variable name with the CLUSTER= option. The default name is CLUSTER.
a new numeric variable, OVER_ALL, containing statistics that apply over all of the VAR variables
the VAR variables giving statistics for particular variables

The values of _TYPE_ for all LEAST= options are given in the following table.

Table 27.2: _TYPE_ Values for all LEAST= Options


_TYPE_	Contents of VAR variables	Contents of OVER_ALL
INITIAL	Initial seeds	Missing

CRITERION	Missing	Optimization criterion; see the LEAST= option; this value is displayed just before the "Cluster Summary" table

CENTER	Cluster centers; see the LEAST= option	Missing

SEED	Cluster seeds: additional information used for imputation

DISPERSION	Dispersion estimates for each cluster; see the LEAST= option; these values are displayed in a separate row with title depending on the LEAST= option	Dispersion estimates pooled over variables; see the LEAST= option; these values are displayed in the "Cluster Summary" table with label depending on the LEAST= option

FREQ	Frequency of each cluster omitting observations with missing values for the VAR variable; these values are not displayed	Frequency of each cluster based on all observations with any nonmissing value; these values are displayed in the "Cluster Summary" table

WEIGHT	Sum of weights for each cluster omitting observations with missing values for the VAR variable; these values are not displayed	Sum of weights for each cluster based on all observations with any nonmissing value; these values are displayed in the "Cluster Summary" table

Observations with _TYPE_='WEIGHT' are included only if you specify the WEIGHT statement.

The _TYPE_ values included only for least-squares clustering are given in the following table. Least-squares clustering is obtained by omitting the LEAST= option or by specifying LEAST=2.

Table 27.3: _TYPE_ Values for Least-Squares Clustering


_TYPE_	Contents of VAR variables	Contents of OVER_ALL
MEAN	Mean for the total sample; this is not displayed	Missing

STD	Standard deviation for the total sample; this is labeled "Total STD" in the output	Standard deviation pooled over all the VAR variables; this is labeled "Total STD" in the output

WITHIN_STD	Pooled within-cluster standard deviation	Within cluster standard deviation pooled over clusters and all the VAR variables

RSQ	R² for predicting the variable from the clusters; this is labeled "R-Squared" in the output	R² pooled over all the VAR variables; this is labeled "R-Squared" in the output

RSQ_RATIO	[(R²)/(1-R²)]; this is labeled "RSQ/(1-RSQ)" in the output	[(R²)/(1-R²)]; labeled "RSQ/(1-RSQ)" in the output

PSEUDO_F	Missing	Pseudo F statistic

ESRQ	Missing	Approximate expected value of R² under the null hypothesis of a single uniform cluster

CCC	Missing	The cubic clustering criterion

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