HDBSCAN - hierarchical density-based clustering for applications with noise
Updated 30 Jun 2018

This is a MATLAB implementation of HDBSCAN, a hierarchical version of DBSCAN. HDBSCAN is described in Campello et al. 2013 and Campello et al. 2015. Please see the extensive documentation in the github repository. Suggestions for improvement / collaborations are encouraged!

Cite As

Jordan Sorokin (2024). Jorsorokin/HDBSCAN (, GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2017b
Compatible with any release
Platform Compatibility
Windows macOS Linux

Inspired by: gaimc : Graph Algorithms In Matlab Code

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Version Published Release Notes

Added "minClustNum" parameter to the HDBSCAN object, which helps realize child clusters in situations where the algorithm finds a few single large clusters but the user disagrees with the results.

Updates to main algorithm for massive speedup (5-10x) by switching away from native matlab "graph" class during fitting. Prediction of new points is also faster and more accurate
Improved performance and memory usage for very large (>15,000 point) data sets. Also added "sparse_to_csr.m", a file by the author of "bfs.m" and "mst_prim.m" for converting sparse matrices

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.