Two-Way Joining Clustering

Performs simultaneous two-way joining of cases (rows) and variables (columns) of a data matrix, and reports and plots the reordered data matrix.

General

Element Name Description
Detail of computed results reported Specifies the detail of computed results reported. If Minimal detail is requested, only the reordered data matrix is plotted and displayed; if All results is requested, descriptive statistics for the rows and column of the data matrix are also reported.
Use user-defined threshold Use user-defined threshold value; if set to False, then the threshold parameter will be computed from the data (as the overall standard deviation divided by 2); if True, specify the threshold value in the field below.

 The threshold parameter determines when the algorithm will consider two numbers in the data matrix to be equal, and thus satisfactorily classified in the same cluster. If this value is very large (relative to the numbers in the data matrix), then only one cluster will be formed; if it is very small, then each data point will represent a cluster by itself.
Threshold value Specifies the user-defined threshold value; this value will be ignored if the Use user-defined threshold option is set to False.

 The threshold parameter determines when the algorithm will consider two numbers in the data matrix to be equal, and thus satisfactorily classified in the same cluster. If this value is very large (relative to the numbers in the data matrix), then only one cluster will be formed; if it is very small, then each data point will represent a cluster by itself.
Missing data deletion Specifies how to treat missing values (observations); missing data can be deleted casewise or substituted by means.
Generates data source, if N for input less than Generates a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations.