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Density Based Clustering (DeBaCl) Toolbox
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======================== DEnsity-BAsed CLustering ======================== 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 <https://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.
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