SPSS® 12.0 Command Syntax Reference

TWOSTEP CLUSTER
Overview
TWOSTEP CLUSTER
[/CATEGORICAL VARIABLES = varlist]
[/CONTINUOUS VARIABLES = varlist]
[/CRITERIA [INITHRESHOLD({0** })] [MXBRANCH({8**})]
{value} {n }
[MXLEVEL({3**})] ]
{n }
[/DISTANCE {EUCLIDEAN }]
{LIKELIHOOD**}
[/HANDLENOISE {0**}]
{n }
[/INFILE FILE = filename]
[/MEMALLOCATE {64**}]
{n }
[/MISSING {EXCLUDE**}]
{INCLUDE }
[/NOSTANDARDIZE [VARIABLES = varlist]]
[/NUMCLUSTERS {AUTO** {15**} [{AIC }]}]
{n } {BIC**}
{FIXED = n }
[/OUTFILE [MODEL = filename] [STATE = filename]]
[/PLOT [BARFREQ] [PIEFREQ]
[VARCHART [COMPARE {BYCLUSTER**}] [NONPARAMETRIC] [CONFIDENCE {95**}] [OMIT] ]]
{BYVAR } {n }
[/PRINT [IC] [COUNT] [SUMMARY]]
[/SAVE CLUSTER [VARIABLE = varname]]
**Default if the subcommand is omitted.
TWOSTEP CLUSTER groups observations into clusters based on a nearness criterion. The
procedure uses a hierarchical agglomerative clustering procedure in which individual cases
are successively combined to form clusters whose centers are far apart. This algorithm is
designed to cluster large numbers of cases. It passes the data once to find the cluster centers
and again to assign cluster memberships. In addition to the benefit of few data passes, the
procedure allows the user to set the amount of memory used by the clustering algorithm.
Basic Features
Cluster Features (CF) Tree. TWOSTEP CLUSTER clusters observations by building a data
structure called the CF tree, which contains the cluster centers. The CF tree is grown during
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