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7 articles
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.
DBSCAN in R · Implementation with dbscan and fpc Packages
Using DBSCAN in R — the dbscan package, kNNdistplot for epsilon selection, fpc::dbscan, and comparison with Python implementations.
DBSCAN for Anomaly Detection · Finding Outliers with Density
How to use DBSCAN for anomaly and outlier detection — the practical approach, advantages over statistical methods, and real-world applications in fraud and network security.
DBSCAN Parameter Selection · Choosing Epsilon and MinPts
A practical guide to choosing DBSCAN's two parameters — the k-distance graph method for epsilon, rules of thumb for minPts, and validation approaches.
Scikit-learn DBSCAN · Implementation Guide
A practical guide to using DBSCAN in scikit-learn — parameter configuration, handling large datasets, extracting cluster labels, and visualising results.
Spatial Indexing for Clustering · R*-trees, kd-trees, and Ball Trees
How spatial indexing accelerates clustering algorithms — the data structures behind efficient neighborhood queries in DBSCAN and OPTICS.