Node Description
Correlation Concatenation
The Correlation Concatenation node is designed to take up to three Input Correlation Matrices and join them into a single Output Correlation Matrix. The user can specify the degree of Correlation each Matrix will have with the other two Matrices when they are joined.
Concatenating Correlation Matrices is useful when the Horizontal Differentiation of Features have been independently generated but some Correlation is known to exist between them. For example, if ‘style’, ‘color’, and ‘ambience’ Features were independently generated, then the Correlation Concatenation node could join these three Features together with some cross-correlation.
All of the row and column names must be unique across all three tables otherwise the Matrices cannot be joined.
This Community Node documentation assumes you have already downloaded the open-source KNIME analytics platform and installed the free Market Simulation (Community Edition) plugin. If not, start by returning to Getting Started.
Downloads
#1 Concatenate Matrices
Inputs
Input Matrix A
Input Correlation Matrix A contains correlation details for the Customer Distributions labelled E, F, and G.
Input Matrix B
Input Correlation Matrix B contains correlation details for the Customer Distributions labelled J, K, and L.
Input Matrix C
Input Correlation Matrix C contains correlation details for the Customer Distributions labelled X, Y, and Z.
Node
Configuration
Set the A:B, A:C, and B:C matrix correlations.
If these values are large (>0.5) then Customer Distribution correlations might be better set using the ‘Differentiation Horizontal’ node.
See also: CN-113 Differentiation Horizontal Node
Outputs
Concatenated Matrix
The correlations from each of the Input Matrices have been preserved. Additional correlation values have been added to the Matrix:Matrix areas.
Repaired Matrix
As Matrix:Matrix correlations set in the Configuration Dialog are low (<0.5) no repairs are required (as can be seen in this Output Error Matrix).
