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DEnsity-BAsed CLustering
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DeBaCl is a Python library for estimation of density level set trees and
nonparametric density-based clustering. Level set trees are based on the
statistically-principled definition of clusters as modes of a probability
density function. They are particularly useful for analyzing structure in
complex datasets that exhibit multi-scale clustering behavior. DeBaCl is
intended to promote the practical use of level set trees through improvements
in computational efficiency, flexible algorithms, and an emphasis on
modularity and user customizability.

The `tutorial for DeBaCl
<http://nbviewer.ipython.org/url/raw.github.com/CoAxLab/DeBaCl/master/docs/debacl_tutorial.ipynb>`_
is an IPython Notebook. It is viewable on nbviewer, or as a PDF at
docs/debacl_tutorial.pdf.

The PDF user manual contains documentation for each function. It can be found
in the GitHub repository at docs/debacl_manual.pdf. A paper describing the
statistical background of level set trees and level set tree clustering is
located in the repository, in the docs/ folder as well.