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Sklearn Mean Shift

Mean shift clustering to find dense regions using scikit-learn

Sklearn Mean Shift

AI-generated

Summary

Assign cluster labels to rows via Mean Shift clustering on selected feature columns.

Inputs

  • featureColumns (required) — Comma-separated list of feature column names
  • bandwidth — Bandwidth (kernel size). Set to 0 for automatic estimation
  • binSeeding — Whether to use binning to speed up the algorithm
  • minBinFreq — Minimum number of points in a bin to seed it
  • pythonPath — Path to Python executable with scikit-learn installed

Output shape

a list of items, each containing the original fields plus 'cluster', 'n_clusters', and 'bandwidth'

If execution fails and continueOnFail is true, an error field may be present instead of the normal fields

Examples

Example 1: Add clustering labels to a dataset using selected features

featureColumns: 'col1,col2', bandwidth: 0 (auto), binSeeding: false

Example 2: Cluster data with bin seeding enabled

featureColumns: 'a,b', bandwidth: 0.5, binSeeding: true, minBinFreq: 3

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