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.

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, the number of dimensions must be lower than or equal to the number of input columns.

Columns

Select the columns that are included by the PCA, i.e the original features.

Remove original data columns

If checked, the columns containing the input data are removed.

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 during the computation.

Views

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Workflows

• 02_Customer_Segmentation_Use_CaseKNIME Hub
• 02_Customer_Segmentation_Use_CaseKNIME Hub
• 02_Customer_Segmentation_Use_CaseKNIME Hub
• 02_Customer_Segmentation_Use_CaseKNIME Hub
• 02_Customer_Segmentation_Use_Case_V2KNIME Hub
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