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).
The p-value for these columns indicates the probability of an
uncorrelated system producing a correlation at least
as extreme, if the mean of the correlation is zero and it
follows a t-distribution with df degrees of freedom.
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.
The p-value for these columns indicates the probability of
independent variables showing as extreme level of dependence.
The value is the same as for a chi-square test
of independence of variables in a contingency table.
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.
Options
Manual Selection
Wildcard/Regex Selection
Popular Successors
- Correlation Filter16 %
- Column Filter5 %
- Partitioning5 %
Row Filter (deprecated)4 %- Scatter Matrix (legacy)3 %
- Excel Writer3 %
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- Statistics1 %
- Heatmap1 %
- Rule-based Row Filter1 %
- Parallel Coordinates (legacy)1 %
- Loop End1 %
- Linear Correlation1 %
- Rank Correlation1 %
- Number Rounder1 %
- Color Manager1 %
- Top k Row Filter< 1 %
- Scatter Plot< 1 %
- Math Formula< 1 %
- Cronbach Alpha< 1 %
CSV Writer< 1 %- Row Sampling< 1 %
- Rule Engine< 1 %
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- Heatmap (JavaScript)< 1 %
- Statistics View< 1 %
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- Low Variance Filter< 1 %
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Scorer< 1 %- Scatter Plot Matrix< 1 %
- Sorter< 1 %
- Table View< 1 %
- Scatter Plot (JavaScript)< 1 %
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- Missing Value< 1 %
- Numeric Outliers< 1 %
- Scatter Plot (Plotly)< 1 %
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- Extract Table Dimension< 1 %
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Table Row to Variable< 1 %Loop End< 1 %- Hierarchical Clustering< 1 %
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Column Rename< 1 %- Numeric Row Splitter< 1 %
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Excel Sheet Appender (XLS)< 1 %- Spark Line Appender< 1 %
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- Lift Chart (JavaScript)< 1 %
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Color Appender< 1 %- Molecule Type Cast< 1 %
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Python Script (1⇒1)< 1 %- Python Script (legacy)< 1 %
Image Writer (Table Column)< 1 %- Model Writer< 1 %
- Loop End (Column Append)< 1 %
- K Nearest Neighbor< 1 %
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- Distance Matrix Pair Extractor< 1 %
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- Category to Number (Apply)< 1 %
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- Lag Column< 1 %
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- Missing Value (Apply)< 1 %
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- DB Loader< 1 %
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- Matrix to Network< 1 %
- Table to R< 1 %
- Date&Time to String< 1 %
- Moving Average< 1 %
XGBoost Linear Ensemble Learner (Regression)< 1 %- RDKit Descriptor Calculation< 1 %
Views
- 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).
Workflows
01_Bike_Restocking_Alert_Training_w_Feature_SelectionKNIME Hub- 01_Data_SummarizationKNIME Hub
- 01_Guided_Labeling_for_Document_ClassificationKNIME Hub
- 01_Guided_Labeling_for_Document_Classification-FIXED-HEADERKNIME Forum
- 01_KNIME_assessment_training_KNIME Hub
- Show all 644 workflows
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