Density-based clustering validation

Density-Based Clustering Validation (DBCV) is a metric designed to assess the quality of clustering solutions, particularly for density-based clustering algorithms like DBSCAN, Mean shift, and OPTICS. This metric is particularly suited for identifying concave and nested clusters, where traditional metrics such as the Silhouette coefficient, Davies–Bouldin index, or Calinski–Harabasz index often struggle to provide meaningful evaluations.

Unlike traditional validation measures, which often rely on compact and well-separated clusters, DBCV index evaluates how well clusters are defined in terms of local density variations and structural coherence.

This metric was introduced in 2014 by David Moulavi and colleagues in their work.^[2] It utilizes density connectivity principles to quantify clustering structures, making it especially effective at detecting arbitrarily shaped clusters in concave datasets, where traditional metrics may be less reliable.

The DBCV index has been employed in bioinformatics analysis,^[3] ecology analysis,^[4] techno-economic analysis,^[5] and health informatics analysis^[6] as well as in numerous other fields.^{[7] [8]}