Calculates for each pair of selected columns a correlation coefficient, i.e. a measure of the correlation of the two variables.

Which correlation measure is applied depends on the types of the
underlying variables:

`numeric <-> numeric`

:
Pearson's product-moment coefficient.
Missing values in a column are ignored in such a way that for the
computation of the correlation between two columns only complete
records are taken into account. For instance, if there are three
columns A, B and C and a row contains a missing value in column A
but not in B and C, then the row will be ignored for computing the
correlation between (A, B) and (A, C). It will not be ignored for
the correlation between (B, C). This corresponds to the function
*cor(<data.frame>, use="pairwise.complete.obs")*
in the R statistics package.

The value of this measure ranges from -1 (strong negative
correlation) to 1 (strong positive correlation). A value of 0
represents no linear correlation (the columns might still be
highly dependent on each other, though).

`nominal <-> nominal`

:
Pearson's chi square test on the contingency table.
This value is then normalized to a range [0,1] using
Cramer's V, whereby 0 represents no correlation and 1
a strong correlation. Missing values in nominal columns are
treated such as they were a self-contained possible value.
If one of the two columns contains more possible values than
specified in the dialog (default 50), the correlation will not
be computed.

Correlation measures for other pairs of columns are not
available, they are represented by missing values in the output
table and crosses in the accompanying view.

- Include
- This list contains the names of those columns in the input table to be included in the output table.
- Exclude
- This list contains the names of those columns in the input table to be excluded from the output table.
- Filter
- Use one of these fields to filter either the Include or Exclude list for certain column names or name substrings.
- Buttons
- Use these buttons to move columns between the Include and Exclude list. Single-arrow buttons will move all selected columns. Double-arrow buttons will move all columns (filtering is taken into account).
- Enforce Inclusion
- Select this option to enforce the current inclusion list to stay the same even if the input table specification changes. If some of the included columns are not available anymore, a warning is displayed. (New columns will automatically be added to the exclusion list.)
- Enforce Exclusion
- Select this option to enforce the current exclusion list to stay the same even if the input table specification changes. If some of the excluded columns are not available anymore, a warning is displayed. (New columns will automatically be added to the inclusion list.)

- Pattern
- Type a search pattern which matches columns to move into the Include or Exclude list. Which list is used can be specified. You can use either Wildcards ('?' matching any character, '*' matching a sequence of any characters) or Regex. You can specify whether your pattern should be case sensitive.

- Possible Values Count
- Select an upper bound for the number of possible values for each of the nominal columns. If more values are encountered in a nominal column, the column will be ignored (no correlation values will be computed).

- Correlation Matrix
- Squared table view showing the pair-wise correlation values of all columns. The color range varies from dark red (strong negative correlation), over white (no correlation) to dark blue (strong positive correlation). If a correlation value for a pair of column is not available, the corresponding cell contains a missing value (shown as cross in the color view).

- 01_AnalyticsKNIME Hub
- 01_AnalyticsKNIME Hub
- 01_Correlation_MatrixKNIME Hub
- 01_Data_PreparationKNIME Hub
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- Feature selectionby mlauber71 on 2019-09-06forum.knime.com/p/69527

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