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JOSS

PyClustering

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/

Dependencies

Required packages: scipy, matplotlib, numpy, Pillow

Python version: >=3.6 (32-bit, 64-bit)

C++ version: >= 14 (32-bit, 64-bit)

Performance

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:

# 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

Installation using pip3 tool:

$ pip3 install pyclustering

Manual installation from official repository using Makefile:

# 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:

# 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:

  1. Clone repository from: https://github.com/annoviko/pyclustering.git
  2. Open folder pyclustering/ccore
  3. Open Visual Studio project ccore.sln
  4. Select solution platform: x86 or x64
  5. Build pyclustering-shared project.
  6. Add pyclustering folder to python path or install it using setup.py
# install pyclustering library
$ python3 setup.py install

# optionally - test the library
$ python3 setup.py test

Proposals, Questions, Bugs

In case of any questions, proposals or bugs related to the pyclustering please contact to [email protected].

Issue tracker: https://github.com/annoviko/pyclustering/issues

Library Content

Clustering algorithms (module pyclustering.cluster):

  • Agglomerative (pyclustering.cluster.agglomerative);
  • BANG (pyclustering.cluster.bang);
  • BIRCH (pyclustering.cluster.birch);
  • BSAS (pyclustering.cluster.bsas);
  • CLARANS (pyclustering.cluster.clarans);
  • CLIQUE (pyclustering.cluster.clique);
  • CURE (pyclustering.cluster.cure);
  • DBSCAN (pyclustering.cluster.dbscan);
  • Elbow (pyclustering.cluster.elbow);
  • EMA (pyclustering.cluster.ema);
  • Fuzzy C-Means (pyclustering.cluster.fcm);
  • GA (Genetic Algorithm) (pyclustering.cluster.ga);
  • G-Means (pyclustering.cluster.gmeans);
  • HSyncNet (pyclustering.cluster.hsyncnet);
  • K-Means (pyclustering.cluster.kmeans);
  • K-Means++ (pyclustering.cluster.center_initializer);
  • K-Medians (pyclustering.cluster.kmedians);
  • K-Medoids (pyclustering.cluster.kmedoids);
  • MBSAS (pyclustering.cluster.mbsas);
  • OPTICS (pyclustering.cluster.optics);
  • ROCK (pyclustering.cluster.rock);
  • Silhouette (pyclustering.cluster.silhouette);
  • SOM-SC (pyclustering.cluster.somsc);
  • SyncNet (pyclustering.cluster.syncnet);
  • Sync-SOM (pyclustering.cluster.syncsom);
  • TTSAS (pyclustering.cluster.ttsas);
  • X-Means (pyclustering.cluster.xmeans);

Oscillatory networks and neural networks (module pyclustering.nnet):

  • Oscillatory network based on Hodgkin-Huxley model (pyclustering.nnet.hhn);
  • fSync: Oscillatory Network based on Landau-Stuart equation and Kuramoto model (pyclustering.nnet.fsync);
  • Hysteresis Oscillatory Network (pyclustering.nnet.hysteresis);
  • LEGION: Local Excitatory Global Inhibitory Oscillatory Network (pyclustering.nnet.legion);
  • PCNN: Pulse-Coupled Neural Network (pyclustering.nnet.pcnn);
  • SOM: Self-Organized Map (pyclustering.nnet.som);
  • Sync: Oscillatory Network based on Kuramoto model (pyclustering.nnet.sync);
  • SyncPR: Oscillatory Network based on Kuramoto model for pattern recognition (pyclustering.nnet.syncpr);
  • SyncSegm: Oscillatory Network based on Kuramoto model for image segmentation (pyclustering.nnet.syncsegm);

Graph Coloring Algorithms (module pyclustering.gcolor):

  • DSATUR (pyclustering.gcolor.dsatur);
  • Hysteresis Oscillatory Network for graph coloring (pyclustering.gcolor.hysteresis);
  • Sync: Oscillatory Network based on Kuramoto model for graph coloring (pyclustering.gcolor.sync);

Containers (module pyclustering.container):

  • CF-Tree (pyclustering.container.cftree);
  • KD-Tree (pyclustering.container.kdtree);

Cite the Library

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:

@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}
}