K-Means Clustering on Dimensionally Reduced Data

This component performs two steps: it first computes a Principal Component Analysis on the selected data and reduce it to a two-dimensional space or a three-dimensional space, depending on the user choice; then, it runs a K-Means algorithm and plots the results in a scatter plot where the axes represent the the principal components and the color the assigned cluster.

Options

Cluster Number

Number of clusters (k value) for the K-Means algorithm.

PCA components

Preferred number of PCA components

Nodes

• Python Edit Variable2 ×
• 3D Scatter Plot (Plotly)1 ×
• CASE Switch Start1 ×
• Color Manager1 ×
• Component Input1 ×
• Show all 12 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME JavaScript Views
• FeatureKNIME Python 2 Integration (legacy)
• FeatureKNIME Quick Forms

Links

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