Density-Based Clustering
DBSCAN
A Practical Field Guide
A focused, independent resource for density-based clustering. Learn how DBSCAN finds arbitrary-shaped clusters, labels noise, chooses epsilon and minPts, and compares with related methods.
Concept
Core points, border points, noise, density reachability, and cluster shape.
Math
Distance metrics, complexity, validation, and high-dimensional behaviour.
Code
Practical Python and R guides, parameter selection, and spatial indexing.
Compare
DBSCAN versus k-means, HDBSCAN, GMM, and hierarchical clustering.
Papers
Annotated readings of Ester et al. 1996 and later density-based research.
Latest entries
DBSCAN for Geospatial Data · Clustering GPS Coordinates
How to cluster geographic data with DBSCAN — Haversine distance, handling latitude and longitude, map visualisation with folium, and real-world spatial analysis.
Scaling DBSCAN to Large Datasets · Approximation Strategies
Practical approaches to clustering millions of points — approximate DBSCAN, sampling, grid-based acceleration, and the limits of exact DBSCAN.
The Ester et al. 1996 Paper · Original DBSCAN Formulation
A summary of the original DBSCAN paper — the key contributions, the algorithm's intellectual context in 1996, and how the original formulation compares with modern implementations.
Gaussian Mixture Models vs DBSCAN · Probabilistic vs Density Clustering
Comparing GMM and DBSCAN — soft clustering vs noise classification, spherical vs arbitrary shapes, and when to use each approach.
Hierarchical Clustering vs DBSCAN · Agglomerative vs Density-Based
Comparing hierarchical (agglomerative) clustering and DBSCAN — dendrograms, arbitrary shapes, noise handling, and the computational cost tradeoffs.
Clustering with High-Dimensional Data · The Curse of Dimensionality
Why clustering fails in high dimensions — distance concentration, the emptiness phenomenon, and strategies for clustering high-dimensional data with DBSCAN.