pyclustering 0.9.0

pyclustring is a python data mining library

PyClustering

pyclustering is a Python, C++ data mining library (clustering algorithm, oscillatory networks, neural networks). The library provides Python and C++ implementations (via CCORE library) of each algorithm or model. CCORE 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/0.9.0/html/index.html

Dependencies

Required packages: scipy, matplotlib, numpy, Pillow

Python version: >=3.5 (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++ is used
xmeans_instance_1 = xmeans(data_points, start_centers, 20, ccore=True);

# The same - C/C++ 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 GCC:

# 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_x64        # build for 64-bit OS

# $ make ccore_x86      # build for 32-bit OS
# $ make ccore          # build for both (32 and 64-bit)

# return to parent folder of the pyclustering library
cd ../

# add current folder to python path
PYTHONPATH=`pwd`
export PYTHONPATH=${PYTHONPATH}

Manual installation using Visual Studio:

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 ‘ccore’ project.

6. Add pyclustering folder to python path.

Proposals, Questions, Bugs

In case of any questions, proposals or bugs related to the pyclustering please contact to [e-mail].

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);

• 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}
}