Hierarchical Clustering

Summary

A clustering method that builds a tree of nested clusters (a dendrogram) rather than requiring you to pick K upfront — useful for exploring data structure at multiple levels of granularity.

How to Learn This

1Build a dendrogram for a small sample dataset and interpret its structure.
2Learn the difference between agglomerative (bottom-up) and divisive (top-down) approaches.
3Compare its output to K-Means on the same dataset.

K-Means Clustering

DBSCAN

More topics in Unsupervised Learning

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

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