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06 Dimensionality Reduction

This workflow shows how to reduce columns based on domain knowledge and via principal component analysis

Dimensionality reduction1. Reduce dimensions based on knowledge of duplicatecolumns (product ID)2. Remove columns with constant values (country)3. Show example of dimensionality reduction based onprincipal component analysis (PCA) Data independent: remove duplicate andredundant columns Data dependent: transform into principal components German credit dataremove rowID, country(duplicate, constant value) CSV Reader PCA PCA Compute PCA Apply Customer data Column Filter Scatter Plot Color Manager Dimensionality reduction1. Reduce dimensions based on knowledge of duplicatecolumns (product ID)2. Remove columns with constant values (country)3. Show example of dimensionality reduction based onprincipal component analysis (PCA) Data independent: remove duplicate andredundant columns Data dependent: transform into principal components German credit dataremove rowID, country(duplicate, constant value)CSV Reader PCA PCA Compute PCA Apply Customer data Column Filter Scatter Plot Color Manager

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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: maarit.widmann
Created at: 2022-10-12
On NodePit since: 2022-11-29
Last update: 2024-04-26
Created with KNIME version: v4.6.4

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