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DBSCAN clustering

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About DBSCAN · from Wikipedia

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu in 1996. It is a density-based clustering algorithm that does not assume a fixed parametric model for the clusters, such as Gaussian blobs, and it does not require the number of clusters to be specified in advance. Given a set of points in some space, it groups together points that are closely packed, and marks as outliers points that lie alone in low-density regions . DBSCAN is one of the most commonly used and cited clustering algorithms.

Summary from Wikipedia ↗, CC BY-SA 4.0.
  • Intro to DBSCAN Mar 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.

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id:DBSCAN
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Intro to DBSCAN