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DBSCAN

Density-Based Spatial Clustering of Applications with Noise; a clustering algorithm that groups together points that are closely packed together.

AbbreviationDBSCAN
Categoryai

Overview

DBSCAN is a popular density-based clustering algorithm. Unlike K-Means, it can find clusters of arbitrary shapes and is robust to outliers.

How it Works

It identifies 'core points' that have a minimum number of neighbors within a specified radius (epsilon). Points that are reachable from core points form a cluster. Points that are not reachable are labeled as noise (outliers).

Advantages

  • Does not require the number of clusters to be specified upfront.
  • Can identify noise/outliers effectively.
  • Can find clusters of non-spherical shapes.

Related Terms

Cluster AnalysisOutlier DetectionHierarchical Clustering