K-Means Clustering

Summary

An algorithm that groups data points into K clusters by minimizing distance to each cluster's center — commonly used for customer segmentation when you don't have predefined labels.

How to Learn This

1Run K-Means on a sample customer dataset and interpret the resulting clusters.
2Learn the 'elbow method' for choosing a reasonable value of K.
3Understand why K-Means is sensitive to feature scaling (connect back to Phase 5).

Model Selection Strategy

Hierarchical Clustering

More topics in Unsupervised Learning

Hierarchical Clustering DBSCAN Principal Component Analysis (PCA)Anomaly Detection Association Rule Mining (Market Basket Analysis)Dimensionality Reduction (t-SNE, UMAP Overview)

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