DBSCAN

Summary

A density-based clustering algorithm that finds clusters of arbitrary shape and automatically identifies outliers as noise — unlike K-Means, it doesn't require specifying the number of clusters upfront.

How to Learn This

1Run DBSCAN on a dataset with non-spherical cluster shapes and compare to K-Means.
2Learn its two key parameters: epsilon (neighborhood radius) and minimum samples.
3Understand why DBSCAN naturally handles outliers, unlike K-Means.

Hierarchical Clustering

Principal Component Analysis (PCA)

More topics in Unsupervised Learning

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

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