Density-based clustering in scenarios with variable density

This repository contains two algorithms devoted to solve different problems regarding variable-density scenarios: K-DBSCAN and V-DBSCAN.


Kernel-Density-Based Spatial Clustering of Applications with Noise (K-DBSCAN) aims at identifying arbitrarily-shaped groups of points within a significantly sparse sample-space, without previous knowledge of the amount of resulting clusters.


Variable-Density-Based Spatial Clustering of Applications with Noise (V-DBSCAN) is a multi-scale variation of DBSCAN that takes into account the variations in density when moving away from the centroid of the data.



  • E. Pla-Sacristán, I. González-Díaz, T. Martínez-Cortés and F. Díaz-de-María, “Finding landmarks within settled areas using hierarchical density-based clustering and meta-data from publicly available images”, Expert Systems with Applications (ESWA). Vol. 123; pp. 315 – 327, 2019; Netherlands. DOI:

Comments are closed.