Sklearn KMeansInstall

Summary

Fit a KMeans clustering model on specified feature columns.

Inputs

• featureColumns (required) — Comma-separated list of feature column names
• nClusters — Number of clusters to form
• init — Method for initialization (k-means++ or random)
• maxIter — Maximum number of iterations
• nInit — Number of times to run with different centroid seeds
• randomState — Random seed for reproducibility
• pythonPath — Path to Python executable with scikit-learn installed

Output shape

single record

A JSON object containing fitted model metadata such as cluster centers, inertia, number of iterations, and the list of feature columns used.

Examples

Example 1: Fit a model on three feature columns with 4 clusters using K-Means++ initialization.

featureColumns='feature1,feature2,feature3', nClusters=4, init='k-means++'

Example 2: Fit a model on the 'age', 'income', and 'spending' columns with 5 clusters and 300 max iterations.

featureColumns='age,income,spending', nClusters=5, maxIter=300, init='k-means++'