<commit_before>.. image:: https://img.shields.io/pypi/v/hdbscan.svg
    :target: https://pypi.python.org/pypi/hdbscan/
    :alt: Version
.. image:: https://img.shields.io/pypi/l/hdbscan.svg
    :target: https://github.com/lmcinnes/hdbscan/blob/master/LICENSE
    :alt: License

=======
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 paper:
    <NAME>, <NAME>, and <NAME>, *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 <http://nbviewer.jupyter.org/github/lmcinnes/hdbscan/blob/master/notebooks/Comparing%20Clustering%20Algorithms.ipynb>`_, explaining `how HDBSCAN works <http://nbviewer.jupyter.org/github/lmcinnes/hdbscan/blob/master/notebooks/How%20HDBSCAN%20Works.ipynb>`_ and `comparing performance with other python clustering implementations <http://nbviewer.jupyter.org/github/lmcinnes/hdbscan/blob/master/notebooks/Benchmarking%20scalability%20of%20clustering%20implementations-v0.7.ipynb>`_ are available.

------------------
How to use HDBSCAN
------------------
<commit_msg>Remember how to do markup ... or lack thereof.<commit_after>.. image:: https://img.shields.io/pypi/v/hdbscan.svg
    :target: https://pypi.python.org/pypi/hdbscan/
    :alt: Version
.. image:: https://img.shields.io/pypi/l/hdbscan.svg
    :target: https://github.com/lmcinnes/hdbscan/blob/master/LICENSE
    :alt: License

=======
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 paper:
    <NAME>, <NAME>, and <NAME>, *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 <http://nbviewer.jupyter.org/github/lmcinnes/hdbscan/blob/master/notebooks/Comparing%20Clustering%20Algorithms.ipynb>`_, explaining `how HDBSCAN works <http://nbviewer.jupyter.org/github/lmcinnes/hdbscan/blob/master/notebooks/How%20HDBSCAN%20Works.ipynb>`_ and `comparing performance with other python clustering implementations <http://nbviewer.jupyter.org/github/lmcinnes/hdbscan/blob/master/notebooks/Benchmarking%20scalability%20of%20clustering%20implementations-v0.7.ipynb>`_ are available.

------------------
How to use HDBSCAN
------------------

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