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PCA w Correlations

Dimensionality Reduction with PCA w/ correlations

This workflow applies PCA dimensionality reduction to reduce the dataset dimensions from three to two features.
Data points are then displayed in a scatter plot. Normalization is included for general use. Linear and rank correlations are calculated.


PCA technique reduces data dimensions to two features, Normalizationunnecessary for this data set, but may be required for others. PCA - 2 dimensionsPlot the extractedprincipal componentsRead Data Set PCA Scatter Plot Linear Correlation Rank Correlation Normalizer Denormalizer File Reader PCA technique reduces data dimensions to two features, Normalizationunnecessary for this data set, but may be required for others. PCA - 2 dimensionsPlot the extractedprincipal componentsRead Data SetPCA Scatter Plot Linear Correlation Rank Correlation Normalizer Denormalizer File Reader

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: rosy
Created at: 2014-10-01
On NodePit since: 2023-05-09
Last update: 2024-04-27
Created with KNIME version: v4.7.2
Tags: scatter plotPCAPrincipal Component Analysisdimensionality reduction

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