This is an implementation of the DBSCAN algorithm as a python extension and simple CLI tool.

In progress but currently works okay on OS X and Python 2.7.

Dependencies

For the CLI tool

• A working C++14 compiler and libc++; Xcode 7+'s tools should be fine on OS X
• Boost's numeric bindings to UBLAS (specifically, #include <boost/numeric/ublas/vector_sparse.hpp> needs to work on your system)

For the Python extension

Setuptools is supposed to manage this when you run install, but if it doesnt, it's the above dependencies plus

• Python 2.7.x (haven't tested Python 3 yet, shouldn't be hard though)
• numpy

For tests

• scikit-learn
• nose

Build

For the CLI tool

make

Python

If you like to test things are working, python setup.py test then

python setup.py install

Run

The CLI tool

usage: dbscan eps min_pts array_type distance_metric precision input_path
eps:        parameter to dbscan algorithm, e.g. 0.3
min_pts:    parameter to dbscan algorithm, e.g. 10
array_type: can be sparse or nonsparse
distance_metric: can be euclidean or cosine
precision:  can be double or single
input_path: is the path of a CSV containing vectors

The Python Extension

There's a great demo in plot.py which I've adapted from one of the scikit-learn ones.