Need help with hdbscan?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

1.9K Stars 349 Forks BSD 3-Clause "New" or "Revised" License 884 Commits 236 Opened issues


A high performance implementation of HDBSCAN clustering.

Services available


Need anything else?

Contributors list

.. image:: :target: :alt: PyPI Version .. image:: :target: :alt: Conda-forge Version .. image:: :target: :alt: Conda-forge downloads .. image:: :target: :alt: License .. image:: :target: :alt: Travis Build Status .. image:: :target: :alt: Test Coverage .. image:: :target: :alt: Docs .. image:: :target: :alt: JOSS article .. image:: :target: :alt: Launch example notebooks in Binder



HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection.

In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster size, is intuitive and easy to select.

HDBSCAN is ideal for exploratory data analysis; it's a fast and robust algorithm that you can trust to return meaningful clusters (if there are any).

Based on the papers:

McInnes L, Healy J. *Accelerated Hierarchical Density Based Clustering* 
In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp 33-42.
2017 `[pdf] `_

R. Campello, D. Moulavi, and J. Sander, Density-Based Clustering Based on Hierarchical Density Estimates In: Advances in Knowledge Discovery and Data Mining, Springer, pp 160-172. 2013

Documentation, including tutorials, are available on ReadTheDocs at .


comparing HDBSCAN to other clustering algorithms 
, explaining
how HDBSCAN works 
comparing performance with other python clustering implementations 
_ are available.

How to use HDBSCAN

The hdbscan package inherits from sklearn classes, and thus drops in neatly next to other sklearn clusterers with an identical calling API. Similarly it supports input in a variety of formats: an array (or pandas dataframe, or sparse matrix) of shape

(num_samples x num_features)
; an array (or sparse matrix) giving a distance matrix between samples.

.. code:: python

import hdbscan
from sklearn.datasets import make_blobs

data, _ = make_blobs(1000)

clusterer = hdbscan.HDBSCAN(min_cluster_size=10) cluster_labels = clusterer.fit_predict(data)


Significant effort has been put into making the hdbscan implementation as fast as possible. It is

orders of magnitude faster than the reference implementation 
_ in Java, and is currently faster than highly optimized single linkage implementations in C and C++.
version 0.7 performance can be seen in this notebook 
_ . In particular
performance on low dimensional data is better than sklearn's DBSCAN 
_ , and via support for caching with joblib, re-clustering with different parameters can be almost free.

Additional functionality

The hdbscan package comes equipped with visualization tools to help you understand your clustering results. After fitting data the clusterer object has attributes for:

  • The condensed cluster hierarchy
  • The robust single linkage cluster hierarchy
  • The reachability distance minimal spanning tree

All of which come equipped with methods for plotting and converting to Pandas or NetworkX for further analysis. See the notebook on

how HDBSCAN works 
_ for examples and further details.

The clusterer objects also have an attribute providing cluster membership strengths, resulting in optional soft clustering (and no further compute expense). Finally each cluster also receives a persistence score giving the stability of the cluster over the range of distance scales present in the data. This provides a measure of the relative strength of clusters.

Outlier Detection

The HDBSCAN clusterer objects also support the GLOSH outlier detection algorithm. After fitting the clusterer to data the outlier scores can be accessed via the

attribute. The result is a vector of score values, one for each data point that was fit. Higher scores represent more outlier like objects. Selecting outliers via upper quantiles is often a good approach.

Based on the paper: R.J.G.B. Campello, D. Moulavi, A. Zimek and J. Sander Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection, ACM Trans. on Knowledge Discovery from Data, Vol 10, 1 (July 2015), 1-51.

Robust single linkage

The hdbscan package also provides support for the robust single linkage clustering algorithm of Chaudhuri and Dasgupta. As with the HDBSCAN implementation this is a high performance version of the algorithm outperforming scipy's standard single linkage implementation. The robust single linkage hierarchy is available as an attribute of the robust single linkage clusterer, again with the ability to plot or export the hierarchy, and to extract flat clusterings at a given cut level and gamma value.

Example usage:

.. code:: python

import hdbscan
from sklearn.datasets import make_blobs

data, _ = make_blobs(1000)

clusterer = hdbscan.RobustSingleLinkage(cut=0.125, k=7) cluster_labels = clusterer.fit_predict(data) hierarchy = clusterer.cluster_hierarchy_ alt_labels = hierarchy.get_clusters(0.100, 5) hierarchy.plot()

Based on the paper: K. Chaudhuri and S. Dasgupta. "Rates of convergence for the cluster tree." In Advances in Neural Information Processing Systems, 2010.


Easiest install, if you have Anaconda (thanks to conda-forge which is awesome!):

.. code:: bash

conda install -c conda-forge hdbscan

PyPI install, presuming you have an up to date pip:

.. code:: bash

pip install hdbscan

Binary wheels for a number of platforms are available thanks to the work of Ryan Helinski [email protected].

If pip is having difficulties pulling the dependencies then we'd suggest to first upgrade pip to at least version 10 and try again:

.. code:: bash

pip install --upgrade pip
pip install hdbscan

Otherwise install the dependencies manually using anaconda followed by pulling hdbscan from pip:

.. code:: bash

conda install cython
conda install numpy scipy
conda install scikit-learn
pip install hdbscan

For a manual install of the latest code directly from GitHub:

.. code:: bash

pip install --upgrade git+

Alternatively download the package, install requirements, and manually run the installer:

.. code:: bash

cd hdbscan-master

pip install -r requirements.txt

python install

Running the Tests

The package tests can be run after installation using the command:

.. code:: bash

nosetests -s hdbscan

or, if

is installed but
is not in your

.. code:: bash

python -m nose -s hdbscan

If one or more of the tests fail, please report a bug at

Python Version

The hdbscan library supports both Python 2 and Python 3. However we recommend Python 3 as the better option if it is available to you.

Help and Support

For simple issues you can consult the

_ in the documentation. If your issue is not suitably resolved there, please check the
_ on github. Finally, if no solution is available there feel free to
open an issue 
_ ; the authors will attempt to respond in a reasonably timely fashion.


We welcome contributions in any form! Assistance with documentation, particularly expanding tutorials, is always welcome. To contribute please

fork the project 
_ make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.


If you have used this codebase in a scientific publication and wish to cite it, please use the

Journal of Open Source Software article 
L. McInnes, J. Healy, S. Astels, *hdbscan: Hierarchical density based clustering*
In: Journal of Open Source Software, The Open Journal, volume 2, number 11.

.. code:: bibtex

  title={hdbscan: Hierarchical density based clustering},
  author={McInnes, Leland and Healy, John and Astels, Steve},
  journal={The Journal of Open Source Software},

To reference the high performance algorithm developed in this library please cite our paper in ICDMW 2017 proceedings.

McInnes L, Healy J. *Accelerated Hierarchical Density Based Clustering* 
In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp 33-42.

.. code:: bibtex

  title={Accelerated Hierarchical Density Based Clustering},
  author={McInnes, Leland and Healy, John},
  booktitle={Data Mining Workshops (ICDMW), 2017 IEEE International Conference on},


The hdbscan package is 3-clause BSD licensed. Enjoy.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.