Chapter 18. Clustering based on density: DBSCAN and OPTICS

Our penultimate stop in unsupervised learning techniques brings us to density-based clustering. Density-based clustering algorithms aim to achieve the same thing as k-means and hierarchical clustering: partitioning a dataset into a finite set of clusters that reveals a grouping structure in our data.

18.1. What is density-based clustering?

18.2. Building your first DBSCAN model

18.3. Building your first OPTICS model

18.4. Strengths and weaknesses of density-based clustering

Summary

Solutions to exercises