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Intro to DBSCAN

Project · teaching material · 2026

Intro to DBSCAN is a self-contained course on DBSCAN (Density-Based Spatial Clustering of Applications with Noise), from the original 1996 KDD paper to real applications. It pairs derivations and documentation with twelve Jupyter notebooks notebooks and a from-scratch implementation in Python.

Why DBSCAN

DBSCAN groups points that are packed closely together and labels isolated points as noise, without being told how many clusters to find. Its relative HDBSCAN clustering is the core of his galactic merger clustering at NARIT, where stars from the same merger event sit in dense clumps of their orbital properties.

Learning paths

PathTimeCovers
Quick start1–2 hoursWhat DBSCAN does, how it differs from k-means, the eps and min_pts parameters
Comprehensive8–12 hoursThe mathematics: ε-neighbourhoods, density-reachability and connectivity, the algorithm step by step
Advanced4–6 hoursParameter tuning, comparison with other algorithms, spatial clustering, anomaly detection, performance

Notebooks

The twelve notebooks run in order, from a quick start to advanced topics. Each concept is traced back to the original paper, whose PDF is included. The src package has the from-scratch implementation, helpers for tuning parameters and loading data, and visualisations. Tests run in GitHub Actions with code coverage.

See also

In the knowledge graph

Intro to DBSCAN and the 9 things it is linked to. Open any dot for its page, or explore it in the full graph.

Galactic merger-history clustering · work · RelatedMerger clusteringPython · skill · Built withPythonGitHub Actions · skill · Built withGitHub ActionsJupyter notebooks · skill · Built withJupyterHDBSCAN clustering · skill · mentionsHDBSCANDBSCAN clustering · skill · Built withDBSCANTeaching and community · theme · ThemesTeaching and comm…Machine learning and data · theme · ThemesMachine learning …Open source on GitHub · person, role or place · ContextOpen source Intro toDBSCAN
9 linked in the graph: 1 work5 skills2 themes1 person, role or place
When
2023202420252026
Mar 2026 · 1 commits

As data

Everything on this page is read from triples in the knowledge graph. These are the 22 it states about Intro to DBSCAN, and 1 more its ontology implies; the console runs SPARQL over the whole graph, here in your browser.

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Works 1
Merger clustering