The following node is available in the Open Source KNIME predictive analytics and data mining platform version 2.7.1. Discover over 1000 other nodes, as well as enterprise functionality at
http://knime.com.
PCA
This node performs a
principal component analysis (PCA) on the given data.
The input data is projected from its original feature space into a space of (possibly) lower
dimension with a minimum of information loss.
Dialog Options
- Target dimensions
-
Select the number of dimensions the input data is projected to.
The number of target dimensions can either be selected directly or by specifying the
minimal amount of information to be preserved.
If selected directly, number of dimensions must be lower or equal than the number of input columns.
- Replace original data columns
-
If checked, the columns containing the input data are removed in the output table
and only the coordinates produces by the projection to the principal components remain.
- Fail if missing values are encountered
-
If checked, execution fails, when the selected columns contain missing values.
By default, rows containing missing values are ignored and not considered in the computation of the principal components.
- Columns
-
Select columns that are included in the analysis of principal components, i.e the original features.
Ports
Output Ports
0 |
Table with input values projected to their principal components |
This node is contained in KNIME Base Nodes
provided by KNIME GmbH, Konstanz, Germany.