Tree Clustering (Joining)

Performs tree clustering (joining); both variables (columns of the data matrix) and cases (rows of the data matrix) can be clustered. You can choose from among various distance measures, clustering algorithms, and amalgamation methods.

General

Element Name Description
Detail of computed results reported Specifies the detail of computed results reported. If Minimal detail is requested, only summary statistics and the summary tree are computed; if All results is requested, spreadsheets showing descriptive statistics and details regarding the amalgamation schedule are also reported.
Cluster cases or variables Specifies whether to cluster the cases or rows of the input data based on the variables or columns, or the variables or columns of the input data based on the cases or rows.
Amalgamation rule Specifies the amalgamation or linkage rule to be used for constructing the tree from the distance matrix.
Distance measure Specifies the distance measure to compute and on which to base the analyses.
Type of tree plot Specifies whether to create a horizontal hierarchical tree plot, a vertical icicle tree plot, or both.
Scale tree to dlink/dmax*100 Select the Scale tree to dlink/dmax*100 option in order for the tree plot to be scaled to a standardized scale (i.e., dlink/dmax*100); if set to False, the scale will be based on the linkage Distance measure.
Missing data deletion Specifies how to treat missing values (observations); missing data can be deleted casewise or substituted by means.