marketertechnologies/pyclustering

pyclustring is a Python, C++ 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 only for Linux, Windows and MacOS operating systems.

Version: 0.10.dev

License: GNU General Public License

E-Mail: [email protected]

Documentation: https://pyclustering.github.io/docs/0.10.0/html/

Homepage: https://pyclustering.github.io/

PyClustering Wiki: https://github.com/annoviko/pyclustering/wiki

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 the Python implementation is used, otherwise C/C++. The implementation can be chosen by ccore flag (by default it is always 'True' and it means that the C/C++ implementation 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);

ccore option runs ccore shared library (core of the pyclustering library). The core is maintained for Linux, Windows and MacOS.

Installation

Installation using pip3 tool:

$ pip3 install pyclustering

Manual installation 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)
# you can specify platform (32-bit: 'ccore_32bit', 64-bit: 'ccore_64bit')
$ cd ccore/
$ make ccore_64bit    # compile CCORE for 64-bit
# make ccore_32bit    # compile CCORE for 32-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 [email protected] or create an issue here.

PyClustering Status

Branch	master	0.10.dev	0.10.0.rel
Build (Linux, MacOS)
Build (Win)
Code Coverage

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}
}

Brief Overview of the Library Content

Clustering algorithms and methods (module pyclustering.cluster):

Algorithm	Python	C++
Agglomerative	✓	✓
BANG	✓
BIRCH	✓
BSAS	✓	✓
CLARANS	✓
CLIQUE	✓	✓
CURE	✓	✓
DBSCAN	✓	✓
Elbow	✓	✓
EMA	✓
Fuzzy C-Means	✓	✓
GA (Genetic Algorithm)	✓	✓
G-Means	✓	✓
HSyncNet	✓	✓
K-Means	✓	✓
K-Means++	✓	✓
K-Medians	✓	✓
K-Medoids	✓	✓
MBSAS	✓	✓
OPTICS	✓	✓
ROCK	✓	✓
Silhouette	✓	✓
SOM-SC	✓	✓
SyncNet	✓	✓
Sync-SOM	✓
TTSAS	✓	✓
X-Means	✓	✓

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

Model	Python	C++
CNN (Chaotic Neural Network)	✓
fSync (Oscillatory network based on Landau-Stuart equation and Kuramoto model)	✓
HHN (Oscillatory network based on Hodgkin-Huxley model)	✓	✓
Hysteresis Oscillatory Network	✓
LEGION (Local Excitatory Global Inhibitory Oscillatory Network)	✓	✓
PCNN (Pulse-Coupled Neural Network)	✓	✓
SOM (Self-Organized Map)	✓	✓
Sync (Oscillatory network based on Kuramoto model)	✓	✓
SyncPR (Oscillatory network for pattern recognition)	✓	✓
SyncSegm (Oscillatory network for image segmentation)	✓	✓

Graph Coloring Algorithms (module pyclustering.gcolor):

Algorithm	Python	C++
DSatur	✓
Hysteresis	✓
GColorSync	✓

Containers (module pyclustering.container):

Algorithm	Python	C++
KD Tree	✓	✓
CF Tree	✓

Examples in the Library

The library contains examples for each algorithm and oscillatory network model:

Clustering examples: pyclustering/cluster/examples

Graph coloring examples: pyclustering/gcolor/examples

Oscillatory network examples: pyclustering/nnet/examples

Code Examples

Data clustering by CURE algorithm

from pyclustering.cluster import cluster_visualizer;
from pyclustering.cluster.cure import cure;
from pyclustering.utils import read_sample;
from pyclustering.samples.definitions import FCPS_SAMPLES;

# Input data in following format [ [0.1, 0.5], [0.3, 0.1], ... ].
input_data = read_sample(FCPS_SAMPLES.SAMPLE_LSUN);

# Allocate three clusters.
cure_instance = cure(input_data, 3);
cure_instance.process();
clusters = cure_instance.get_clusters();

# Visualize allocated clusters.
visualizer = cluster_visualizer();
visualizer.append_clusters(clusters, input_data);
visualizer.show();

Data clustering by K-Means algorithm

from pyclustering.cluster.kmeans import kmeans, kmeans_visualizer
from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer
from pyclustering.samples.definitions import FCPS_SAMPLES
from pyclustering.utils import read_sample

# Load list of points for cluster analysis.
sample = read_sample(FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS)

# Prepare initial centers using K-Means++ method.
initial_centers = kmeans_plusplus_initializer(sample, 2).initialize()

# Create instance of K-Means algorithm with prepared centers.
kmeans_instance = kmeans(sample, initial_centers)

# Run cluster analysis and obtain results.
kmeans_instance.process()
clusters = kmeans_instance.get_clusters()
final_centers = kmeans_instance.get_centers()

# Visualize obtained results
kmeans_visualizer.show_clusters(sample, clusters, final_centers)

Data clustering by OPTICS algorithm

from pyclustering.cluster import cluster_visualizer
from pyclustering.cluster.optics import optics, ordering_analyser, ordering_visualizer
from pyclustering.samples.definitions import FCPS_SAMPLES
from pyclustering.utils import read_sample

# Read sample for clustering from some file
sample = read_sample(FCPS_SAMPLES.SAMPLE_LSUN)

# Run cluster analysis where connectivity radius is bigger than real
radius = 2.0
neighbors = 3
amount_of_clusters = 3
optics_instance = optics(sample, radius, neighbors, amount_of_clusters)

# Performs cluster analysis
optics_instance.process()

# Obtain results of clustering
clusters = optics_instance.get_clusters()
noise = optics_instance.get_noise()
ordering = optics_instance.get_ordering()

# Visualize ordering diagram
analyser = ordering_analyser(ordering)
ordering_visualizer.show_ordering_diagram(analyser, amount_of_clusters)

# Visualize clustering results
visualizer = cluster_visualizer()
visualizer.append_clusters(clusters, sample)
visualizer.show()

Simulation of oscillatory network PCNN

from pyclustering.nnet.pcnn import pcnn_network, pcnn_visualizer

# Create Pulse-Coupled neural network with 10 oscillators.
net = pcnn_network(10)

# Perform simulation during 100 steps using binary external stimulus.
dynamic = net.simulate(50, [1, 1, 1, 0, 0, 0, 0, 1, 1, 1])

# Allocate synchronous ensembles from the output dynamic.
ensembles = dynamic.allocate_sync_ensembles()

# Show output dynamic.
pcnn_visualizer.show_output_dynamic(dynamic, ensembles)

Simulation of chaotic neural network CNN

from pyclustering.cluster import cluster_visualizer
from pyclustering.samples.definitions import SIMPLE_SAMPLES
from pyclustering.utils import read_sample
from pyclustering.nnet.cnn import cnn_network, cnn_visualizer

# Load stimulus from file.
stimulus = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3)

# Create chaotic neural network, amount of neurons should be equal to amount of stimulus.
network_instance = cnn_network(len(stimulus))

# Perform simulation during 100 steps.
steps = 100
output_dynamic = network_instance.simulate(steps, stimulus)

# Display output dynamic of the network.
cnn_visualizer.show_output_dynamic(output_dynamic)

# Display dynamic matrix and observation matrix to show clustering phenomenon.
cnn_visualizer.show_dynamic_matrix(output_dynamic)
cnn_visualizer.show_observation_matrix(output_dynamic)

# Visualize clustering results.
clusters = output_dynamic.allocate_sync_ensembles(10)
visualizer = cluster_visualizer()
visualizer.append_clusters(clusters, stimulus)
visualizer.show()

Illustrations

Cluster allocation on FCPS dataset collection by DBSCAN:

Cluster allocation by OPTICS using cluster-ordering diagram:

Partial synchronization (clustering) in Sync oscillatory network:

Cluster visualization by SOM (Self-Organized Feature Map)