  The level set tree approach of Hartigan (1975) provides a probabilistically
based and highly interpretable encoding of the clustering behavior of a
dataset. By representing the hierarchy of data modes as a dendrogram of the
level sets of a density estimator, this approach offers many advantages for
exploratory analysis and clustering, especially for complex and
high-dimensional data. Several R packages exist for level set tree estimation,
but their practical usefulness is limited by computational inefficiency,
absence of interactive graphical capabilities and, from a theoretical
perspective, reliance on asymptotic approximations. To make it easier for
practitioners to capture the advantages of level set trees, we have written the
Python package DeBaCl for DEnsity-BAsed CLustering. In this article we
illustrate how DeBaCl's level set tree estimates can be used for difficult
clustering tasks and interactive graphical data analysis. The package is
intended to promote the practical use of level set trees through improvements
in computational efficiency and a high degree of user customization. In
addition, the flexible algorithms implemented in DeBaCl enjoy finite sample
accuracy, as demonstrated in recent literature on density clustering. Finally,
we show the level set tree framework can be easily extended to deal with
functional data.
