Sklearn KMeansInstall

Summary

Fit a KMeans model to the input data and assign each item to a cluster.

Inputs

• main (required) — Input data items. Each item must contain numeric values for the columns specified in Feature Columns.

Output shape

same as input

Each output item contains all original fields plus a new cluster field with the assigned cluster ID. The first item additionally includes model (JSON string of the Python result), cluster_centers, and inertia for reuse in downstream nodes.

Examples

Example 1: Cluster customer records based on age and income.

Set Feature Columns to 'age,income', keep default K=3, use k-means++ initialization.