Package Health Score

52 / 100

security
Security review needed
popularity
Popular
maintenance
Inactive
community
Sustainable

Popularity

Popular

GitHub Stars: 1.17K
Forks: 248
Contributors: 20

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package pyclustering, we found that it has been starred 1,172 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Nov 25, 2020
Direct Vulnerabilities: 0
C
0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

BSD-3-Clause

Security Policy

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Maintenance

Inactive

Commit Frequency

Open Issues: 69
Open PR: 7
Last Release: 4 years ago
Last Commit: 9 months ago

Further analysis of the maintenance status of pyclustering based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for pyclustering is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Sustainable

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 20
Funding: No

With more than 10 contributors for the pyclustering repository, this is possibly a sign for a growing and inviting community.

We found a way for you to contribute to the project! Looks like pyclustering is missing a Code of Conduct.

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Package

Python Versions Compatibility: >=3.6

Age: 8 years
Latest Release: 4 years ago
Dependencies: N/A
Versions: 17
Maintainers: 1
Wheels: No

Not sure how to use pyclustering?

To help you get started, we've collected the most common ways that pyclustering is being used within popular public projects.

annoviko / pyclustering / pyclustering / clustering / dbscan / tests.py View on Github

def testLengthProcessedSampleSimple1(self):    
        self.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 0.7, 0, 10);
        self.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 0.5, 0, 10);

View more ways to use pyclustering

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:

Documentation:

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:

Clone repository from:
Open folder pyclustering/ccore
Open Visual Studio project ccore.sln
Select solution platform: x86 or x64
Build pyclustering-shared project.
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 .

Issue tracker:

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: .

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}
}

FAQs

What is pyclustering?

pyclustring is a python data mining library. Visit Snyk Advisor to see a full health score report for pyclustering, including popularity, security, maintenance & community analysis.

Is pyclustering popular?

The python package pyclustering receives a total of 19,761 weekly downloads. As such, pyclustering popularity was classified as a popular. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is pyclustering well maintained?

We found indications that pyclustering is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is pyclustering safe to use?

While scanning the latest version of pyclustering, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 13 November-2024, at 14:08 (UTC).

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pyclustering

pyclustring is a python data mining library For more information about how to use this package see README

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Package Health Score

Popularity

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Security

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Maintenance

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Community

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Package

Not sure how to use pyclustering?

PyClustering

Dependencies

Performance

Installation

Proposals, Questions, Bugs

Library Content

Cite the Library

FAQs

What is pyclustering?

Is pyclustering popular?

Is pyclustering well maintained?

Is pyclustering safe to use?

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Package Health Score

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Commit Frequency

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Not sure how to use pyclustering?

PyClustering

Dependencies

Performance

Installation

Proposals, Questions, Bugs

Library Content

Cite the Library

FAQs

What is pyclustering?

Is pyclustering popular?

Is pyclustering well maintained?

Is pyclustering safe to use?

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Source Code

Open Source

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Config

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