DeBaCl is a Python library for density-based clustering with level set trees.

Level set trees are a statistically-principled way to represent the topology of a probability density function. This representation is particularly useful for several core tasks in statistics:

• clustering, especially for data with multi-scale clustering behavior
• describing data topology
• exploratory data analysis
• data visualization
• anomaly detection

DeBaCl is a Python implementation of the Level Set Tree method, with an emphasis on computational speed, algorithmic simplicity, and extensibility.

DeBaCl is available under the 3-clause BSD license.

DeBaCl is currently compatible with Python 2.7 only. Other versions may work, but caveat emptor; at this time DeBaCl is only officially tested on Python 2.7. The package can be downloaded and installed from the Python package installer. From a terminal:

$ pip install debacl

It can also be installed by cloning this GitHub repo. This requires updating the Python path to include the cloned repo. On linux, this looks something like:

$ git clone https://github.com/CoAxLab/DeBaCl/
$ export PYTHONPATH='DeBaCl'

All of the dependencies are Python packages that can be installed with either conda or pip. DeBaCl 1.0 no longer depends on igraph, which required tricky manual installation.

Langauges:

• Python 2.7
• (coming soon: Python 3.4)

Required packages:

• numpy
• networkx
• prettytable

Strongly recommended packages

• matplotlib
• scipy

Optional packages

• scikit-learn

X = make_moons(n_samples=100, noise=0.1, random_state=19)[0]

tree = dcl.construct_tree(X, k=10, prune_threshold=10) print tree

```no-highlight
+----+-------------+-----------+------------+----------+------+--------+----------+
| id | start_level | end_level | start_mass | end_mass | size | parent | children |
+----+-------------+-----------+------------+----------+------+--------+----------+
| 0  |    0.000    |   0.196   |   0.000    |  0.220   | 100  |  None  |  [1, 2]  |
| 1  |    0.196    |   0.396   |   0.220    |  0.940   |  37  |   0    |    []    |
| 2  |    0.196    |   0.488   |   0.220    |  1.000   |  41  |   0    |    []    |
+----+-------------+-----------+------------+----------+------+--------+----------+