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SPSS® 12.0 Command Syntax Reference

SPSS® 12.0 Command Syntax Reference

SPSS® 12.0 Command Syntax Reference

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236 CLUSTER<br />

BSHAPE[(p[,np])] Binary shape difference. This dissimilarity measure has no upper or<br />

lower limit.<br />

DISPER[(p[,np])] Dispersion similarity measure. The range is −1 to +1.<br />

VARIANCE[(p[,np])] Variance dissimilarity measure. This measure has a minimum value<br />

of 0 and no upper limit.<br />

BLWMN[(p[,np])] Binary Lance-and-Williams nonmetric dissimilarity measure. This<br />

measure is also known as the Bray-Curtis nonmetric coefficient. The<br />

range is 0 to 1.<br />

METHOD Subcommand<br />

PATTERN( x, y)<br />

BSHAPE( x, y)<br />

DISPER( x, y)<br />

VARIANCE( x, y)<br />

BLWMN( x, y)<br />

METHOD specifies one or more clustering methods.<br />

• If the METHOD subcommand is omitted or included without specifications, the method of<br />

average linkage between groups is used.<br />

• Only one METHOD subcommand can be used, but more than one method can be specified<br />

on it.<br />

• When the number of items is large, CENTROID and MEDIAN require significantly more<br />

CPU time than other methods.<br />

BAVERAGE Average linkage between groups (UPGMA). BAVERAGE is the default and<br />

can also be requested with keyword DEFAULT.<br />

WAVERAGE Average linkage within groups.<br />

bc<br />

( a+ b+ c + d)<br />

2<br />

= -------------------------------------<br />

SINGLE Single linkage or nearest neighbor.<br />

COMPLETE Complete linkage or furthest neighbor.<br />

( a+ b+ c + d)<br />

( b+ c)<br />

( b– c)<br />

2 –<br />

( a + b + c + d)<br />

2<br />

= -----------------------------------------------------------------------------<br />

ad – bc<br />

( a+ b+ c+ d)<br />

2<br />

= -------------------------------------<br />

b + c<br />

= --------------------------------------<br />

4( a+ b+ c+ d)<br />

b+ c<br />

=<br />

-----------------------<br />

2a + b + c<br />

CENTROID Centroid clustering (UPGMC). Squared Euclidean distances are commonly<br />

used with this method.

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