|JOSS|
pyclustering is a Python, C++ data mining library (clustering algorithm, oscillatory networks, neural networks). The library provides Python and C++ implementations (C++ pyclustering library) of each algorithm or model. C++ pyclustering library is a part of pyclustering and supported for Linux, Windows and MacOS operating systems.
Official repository: https://github.com/annoviko/pyclustering/
Documentation: https://pyclustering.github.io/docs/0.10.1/html/
Required packages: scipy, matplotlib, numpy, Pillow
Python version: >=3.6 (32-bit, 64-bit)
C++ version: >= 14 (32-bit, 64-bit)
Each algorithm is implemented using Python and C/C++ language, if your platform is not supported then Python
implementation is used, otherwise C/C++. Implementation can be chosen by ccore flag (by default it is always
'True' and it means that C/C++ is used), for example:
.. code:: python
# As by default - C/C++ part of the library is used
xmeans_instance_1 = xmeans(data_points, start_centers, 20, ccore=True);
# The same - C/C++ part of the library is used by default
xmeans_instance_2 = xmeans(data_points, start_centers, 20);
# Switch off core - Python is used
xmeans_instance_3 = xmeans(data_points, start_centers, 20, ccore=False);
Installation using pip3 tool:
.. code:: bash
$ pip3 install pyclustering
Manual installation from official repository using Makefile:
.. code:: bash
# get sources of the pyclustering library, for example, from repository
$ mkdir pyclustering
$ cd pyclustering/
$ git clone https://github.com/annoviko/pyclustering.git .
# compile CCORE library (core of the pyclustering library).
$ cd ccore/
$ make ccore_64bit # build for 64-bit OS
# $ make ccore_32bit # build for 32-bit OS
# return to parent folder of the pyclustering library
$ cd ../
# install pyclustering library
$ python3 setup.py install
# optionally - test the library
$ python3 setup.py test
Manual installation using CMake:
.. code:: bash
# get sources of the pyclustering library, for example, from repository
$ mkdir pyclustering
$ cd pyclustering/
$ git clone https://github.com/annoviko/pyclustering.git .
# generate build files.
$ mkdir build
$ cmake ..
# build pyclustering-shared target depending on what was generated (Makefile or MSVC solution)
# if Makefile has been generated then
$ make pyclustering-shared
# return to parent folder of the pyclustering library
$ cd ../
# install pyclustering library
$ python3 setup.py install
# optionally - test the library
$ python3 setup.py test
Manual installation using Microsoft Visual Studio solution:
pyclustering/ccoreccore.slnx86 or x64pyclustering-shared project... code:: bash
# install pyclustering library
$ python3 setup.py install
# optionally - test the library
$ python3 setup.py test
In case of any questions, proposals or bugs related to the pyclustering please contact to pyclustering@yandex.ru.
Issue tracker: https://github.com/annoviko/pyclustering/issues
Clustering algorithms (module pyclustering.cluster):
Oscillatory networks and neural networks (module pyclustering.nnet):
Graph Coloring Algorithms (module pyclustering.gcolor):
Containers (module pyclustering.container):
If you are using pyclustering library in a scientific paper, please, cite the library:
Novikov, A., 2019. PyClustering: Data Mining Library. Journal of Open Source Software, 4(36), p.1230. Available at: http://dx.doi.org/10.21105/joss.01230.
BibTeX entry:
.. code::
@article{Novikov2019,
doi = {10.21105/joss.01230},
url = {https://doi.org/10.21105/joss.01230},
year = 2019,
month = {apr},
publisher = {The Open Journal},
volume = {4},
number = {36},
pages = {1230},
author = {Andrei Novikov},
title = {{PyClustering}: Data Mining Library},
journal = {Journal of Open Source Software}
}
.. |JOSS| image:: http://joss.theoj.org/papers/10.21105/joss.01230/status.svg :target: https://doi.org/10.21105/joss.01230
FAQs
pyclustring is a python data mining library
We found that pyclustering demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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