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https://github.com/scikit-learn-contrib/hdbscan

A high performance implementation of HDBSCAN clustering.
https://github.com/scikit-learn-contrib/hdbscan

cluster-analysis clustering clustering-algorithm clustering-evaluation machine-learning machine-learning-algorithms

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A high performance implementation of HDBSCAN clustering.

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README 

        
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=======

HDBSCAN

=======



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 http://hdbscan.readthedocs.io/en/latest/ .  

    

Notebooks `comparing HDBSCAN to other clustering algorithms `_, explaining `how HDBSCAN works `_ and `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)



-----------

Performance

-----------



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

``outlier_scores_`` 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.



----------------

Branch detection

----------------



The hdbscan package supports a branch-detection post-processing step 

by `Bot et al. `_. Cluster shapes,

such as branching structures, can reveal interesting patterns 

that are not expressed in density-based cluster hierarchies. The 

BranchDetector class mimics the HDBSCAN API and can be used to

detect branching hierarchies in clusters. It provides condensed 

branch hierarchies, branch persistences, and branch memberships and 

supports joblib's caching functionality. A notebook 

`demonstrating the BranchDetector is available `_.



Example usage:



.. code:: python



    import hdbscan

    from sklearn.datasets import make_blobs



    data, _ = make_blobs(1000)



    clusterer = hdbscan.HDBSCAN(branch_detection_data=True).fit(data)

    branch_detector = hdbscan.BranchDetector().fit(clusterer)

    branch_detector.cluster_approximation_graph_.plot(edge_width=0.1)



Based on the paper:

    D. M. Bot, J. Peeters, J. Liesenborgs and J. Aerts

    *"FLASC: A Flare-Sensitive Clustering Algorithm: Extending HDBSCAN\* for Detecting Branches in Clusters"*

    Arxiv 2311.15887, 2023.



----------

Installing

----------



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 .



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+https://github.com/scikit-learn-contrib/hdbscan.git#egg=hdbscan



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



.. code:: bash



    wget https://github.com/scikit-learn-contrib/hdbscan/archive/master.zip

    unzip master.zip

    rm master.zip

    cd hdbscan-master

    

    pip install -r requirements.txt

    

    python setup.py install



-----------------

Running the Tests

-----------------



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



.. code:: bash



    nosetests -s hdbscan



or, if ``nose`` is installed but ``nosetests`` is not in your ``PATH`` variable:



.. code:: bash



    python -m nose -s hdbscan



If one or more of the tests fail, please report a bug at https://github.com/scikit-learn-contrib/hdbscan/issues/new



--------------

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 `FAQ `_ in the documentation.

If your issue is not suitably resolved there, please check the `issues `_ 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.



------------

Contributing

------------



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.



------

Citing

------



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.

    2017

    

.. code:: bibtex



    @article{mcinnes2017hdbscan,

      title={hdbscan: Hierarchical density based clustering},

      author={McInnes, Leland and Healy, John and Astels, Steve},

      journal={The Journal of Open Source Software},

      volume={2},

      number={11},

      pages={205},

      year={2017}

    }

    

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.

    2017



.. code:: bibtex



    @inproceedings{mcinnes2017accelerated,

      title={Accelerated Hierarchical Density Based Clustering},

      author={McInnes, Leland and Healy, John},

      booktitle={Data Mining Workshops (ICDMW), 2017 IEEE International Conference on},

      pages={33--42},

      year={2017},

      organization={IEEE}

    }



If you used the branch-detection functionality in this codebase in a scientific publication and which to cite it, please use the `Arxiv preprint `_: 



    D. M. Bot, J. Peeters, J. Liesenborgs and J. Aerts

    *"FLASC: A Flare-Sensitive Clustering Algorithm: Extending HDBSCAN\* for Detecting Branches in Clusters"*

    Arxiv 2311.15887, 2023.



.. code:: bibtex



    @misc{bot2023flasc,

        title={FLASC: A Flare-Sensitive Clustering Algorithm: Extending HDBSCAN* for Detecting Branches in Clusters}, 

        author={D. M. Bot and J. Peeters and J. Liesenborgs and J. Aerts},

        year={2023},

        eprint={2311.15887},

        archivePrefix={arXiv},

        primaryClass={cs.LG},

        url={https://arxiv.org/abs/2311.15887}, 

    }



---------

Licensing

---------



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