fuzzy-c-means


Namefuzzy-c-means JSON
Version 1.7.0 PyPI version JSON
download
home_pagehttps://github.com/omadson/fuzzy-c-means
SummaryA simple python implementation of Fuzzy C-means algorithm.
upload_time2022-12-09 15:04:58
maintainer
docs_urlNone
authorMadson Dias
requires_python>=3.9,<4.0
licenseMIT
keywords machine-learning data-science fuzzy-c-means clustering
VCS
bugtrack_url
requirements click colorama joblib numpy pydantic tabulate tqdm typer typing-extensions
Travis-CI No Travis.
coveralls test coverage No coveralls. 
            # fuzzy-c-means

![GitHub](https://img.shields.io/github/license/omadson/fuzzy-c-means.svg)
[![PyPI](https://img.shields.io/pypi/v/fuzzy-c-means.svg)](http://pypi.org/project/fuzzy-c-means/)
[![Documentation Status](https://readthedocs.org/projects/fuzzy-c-means/badge/?version=latest)](https://fuzzy-c-means.readthedocs.io/en/latest/?badge=latest)
[![GitHub last commit](https://img.shields.io/github/last-commit/omadson/fuzzy-c-means.svg)](https://github.com/omadson/fuzzy-c-means/commit/master)
[![Downloads](https://pepy.tech/badge/fuzzy-c-means)](https://pepy.tech/project/fuzzy-c-means)
[![DOI](https://zenodo.org/badge/186457481.svg)](https://zenodo.org/badge/latestdoi/186457481)


**[Documentation](https://fuzzy-c-means.readthedocs.io/)** | **[Changelog](https://fuzzy-c-means.readthedocs.io/en/latest/CHANGELOG/)** | **[Citation](https://fuzzy-c-means.readthedocs.io/en/latest/citation/)**



`fuzzy-c-means` is a Python module implementing the [Fuzzy C-means][1] clustering algorithm.

## installation
the `fuzzy-c-means` package is available in [PyPI](https://pypi.org/project/fuzzy-c-means/). to install, simply type the following command:
```
pip install fuzzy-c-means
```
## citation
if you use `fuzzy-c-means` package in your paper, please cite it in your publication.
```
@software{dias2019fuzzy,
  author       = {Madson Luiz Dantas Dias},
  title        = {fuzzy-c-means: An implementation of Fuzzy $C$-means clustering algorithm.},
  month        = may,
  year         = 2019,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.3066222},
  url          = {https://git.io/fuzzy-c-means}
}
```

<!-- ### citations
 - [Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH](https://doi.org/10.1177/1177932220909851)
 - [Analisis Data Log IDS Snort dengan Algoritma Clustering Fuzzy C-Means](https://doi.org/10.24843/MITE.2020.v19i01.P14)
 - [Comparative Analysis between the k-means and Fuzzy c-means Algorithms to Detect UDP Flood DDoS Attack on a SDN/NFV Environment](https://doi.org/10.5220/0010176201050112)
 - [Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data](https://arxiv.org/abs/1910.07763)
 - [Fuzzy Clustering: an Application to Distributional Reinforcement Learning](https://doi.org/10.34726/hss.2021.86783)
 - [Fuzzy Clustering with Similarity Queries](https://arxiv.org/pdf/2106.02212.pdf)
 - [Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants](https://arxiv.org/abs/2110.09622)
 - [Unsupervised clustering-based spectral analysis of bio-dyed textile samples](http://urn.fi/urn:nbn:fi:uef-20211291) -->


## contributing and support

this project is open for contributions. here are some of the ways for you to contribute:
 - bug reports/fix
 - features requests
 - use-case demonstrations

please open an [issue](https://github.com/omadson/fuzzy-c-means/issues) with enough information for us to reproduce your problem. A [minimal, reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) would be very helpful.

to make a contribution, just fork this repository, push the changes in your fork, open up an issue, and make a pull request!

<!-- ## contributors
 - [Madson Dias](https://github.com/omadson)
 - [Dirk Nachbar](https://github.com/dirknbr)
 - [Alberth Florêncio](https://github.com/zealberth) -->

[1]: https://doi.org/10.1016/0098-3004(84)90020-7
[2]: http://scikit-learn.org/

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/omadson/fuzzy-c-means",
    "name": "fuzzy-c-means",
    "maintainer": "",
    "docs_url": null,
    "requires_python": ">=3.9,<4.0",
    "maintainer_email": "",
    "keywords": "machine-learning,data-science,fuzzy-c-means,clustering",
    "author": "Madson Dias",
    "author_email": "madsonddias@gmail.com",
    "download_url": "https://files.pythonhosted.org/packages/9e/a1/fe18a9cb3a65fe0fbe2e96ec6acf0753a09fe4d24868f42530f7db5df22d/fuzzy_c_means-1.7.0.tar.gz",
    "platform": null,
    "description": "# fuzzy-c-means\n\n![GitHub](https://img.shields.io/github/license/omadson/fuzzy-c-means.svg)\n[![PyPI](https://img.shields.io/pypi/v/fuzzy-c-means.svg)](http://pypi.org/project/fuzzy-c-means/)\n[![Documentation Status](https://readthedocs.org/projects/fuzzy-c-means/badge/?version=latest)](https://fuzzy-c-means.readthedocs.io/en/latest/?badge=latest)\n[![GitHub last commit](https://img.shields.io/github/last-commit/omadson/fuzzy-c-means.svg)](https://github.com/omadson/fuzzy-c-means/commit/master)\n[![Downloads](https://pepy.tech/badge/fuzzy-c-means)](https://pepy.tech/project/fuzzy-c-means)\n[![DOI](https://zenodo.org/badge/186457481.svg)](https://zenodo.org/badge/latestdoi/186457481)\n\n\n**[Documentation](https://fuzzy-c-means.readthedocs.io/)** | **[Changelog](https://fuzzy-c-means.readthedocs.io/en/latest/CHANGELOG/)** | **[Citation](https://fuzzy-c-means.readthedocs.io/en/latest/citation/)**\n\n\n\n`fuzzy-c-means` is a Python module implementing the [Fuzzy C-means][1] clustering algorithm.\n\n## installation\nthe `fuzzy-c-means` package is available in [PyPI](https://pypi.org/project/fuzzy-c-means/). to install, simply type the following command:\n```\npip install fuzzy-c-means\n```\n## citation\nif you use `fuzzy-c-means` package in your paper, please cite it in your publication.\n```\n@software{dias2019fuzzy,\n  author       = {Madson Luiz Dantas Dias},\n  title        = {fuzzy-c-means: An implementation of Fuzzy $C$-means clustering algorithm.},\n  month        = may,\n  year         = 2019,\n  publisher    = {Zenodo},\n  doi          = {10.5281/zenodo.3066222},\n  url          = {https://git.io/fuzzy-c-means}\n}\n```\n\n<!-- ### citations\n - [Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH](https://doi.org/10.1177/1177932220909851)\n - [Analisis Data Log IDS Snort dengan Algoritma Clustering Fuzzy C-Means](https://doi.org/10.24843/MITE.2020.v19i01.P14)\n - [Comparative Analysis between the k-means and Fuzzy c-means Algorithms to Detect UDP Flood DDoS Attack on a SDN/NFV Environment](https://doi.org/10.5220/0010176201050112)\n - [Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data](https://arxiv.org/abs/1910.07763)\n - [Fuzzy Clustering: an Application to Distributional Reinforcement Learning](https://doi.org/10.34726/hss.2021.86783)\n - [Fuzzy Clustering with Similarity Queries](https://arxiv.org/pdf/2106.02212.pdf)\n - [Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants](https://arxiv.org/abs/2110.09622)\n - [Unsupervised clustering-based spectral analysis of bio-dyed textile samples](http://urn.fi/urn:nbn:fi:uef-20211291) -->\n\n\n## contributing and support\n\nthis project is open for contributions. here are some of the ways for you to contribute:\n - bug reports/fix\n - features requests\n - use-case demonstrations\n\nplease open an [issue](https://github.com/omadson/fuzzy-c-means/issues) with enough information for us to reproduce your problem. A [minimal, reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) would be very helpful.\n\nto make a contribution, just fork this repository, push the changes in your fork, open up an issue, and make a pull request!\n\n<!-- ## contributors\n - [Madson Dias](https://github.com/omadson)\n - [Dirk Nachbar](https://github.com/dirknbr)\n - [Alberth Flor\u00eancio](https://github.com/zealberth) -->\n\n[1]: https://doi.org/10.1016/0098-3004(84)90020-7\n[2]: http://scikit-learn.org/\n\n",
    "bugtrack_url": null,
    "license": "MIT",
    "summary": "A simple python implementation of Fuzzy C-means algorithm.",
    "version": "1.7.0",
    "split_keywords": [
        "machine-learning",
        "data-science",
        "fuzzy-c-means",
        "clustering"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "a1f242922e57210ed73b7c547d3daf60",
                "sha256": "40b776b549f56cd9758776638fde07b2d839899f1d2e8fcc71d74d82de46af5e"
            },
            "downloads": -1,
            "filename": "fuzzy_c_means-1.7.0-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "a1f242922e57210ed73b7c547d3daf60",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.9,<4.0",
            "size": 9047,
            "upload_time": "2022-12-09T15:04:55",
            "upload_time_iso_8601": "2022-12-09T15:04:55.453252Z",
            "url": "https://files.pythonhosted.org/packages/7f/38/3376b99d66a4d47810ac650b0d7a21a08caa021a691bc809be3d46f9a7a9/fuzzy_c_means-1.7.0-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "md5": "32d197c4c6e079e70c0cd2d256ec386e",
                "sha256": "c68dcc0931b49d1d76c71bfc6cf49782cb2d3d6b852d94ac2b162367e3c58fe5"
            },
            "downloads": -1,
            "filename": "fuzzy_c_means-1.7.0.tar.gz",
            "has_sig": false,
            "md5_digest": "32d197c4c6e079e70c0cd2d256ec386e",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.9,<4.0",
            "size": 8454,
            "upload_time": "2022-12-09T15:04:58",
            "upload_time_iso_8601": "2022-12-09T15:04:58.295003Z",
            "url": "https://files.pythonhosted.org/packages/9e/a1/fe18a9cb3a65fe0fbe2e96ec6acf0753a09fe4d24868f42530f7db5df22d/fuzzy_c_means-1.7.0.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2022-12-09 15:04:58",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "github_user": "omadson",
    "github_project": "fuzzy-c-means",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "requirements": [
        {
            "name": "click",
            "specs": [
                [
                    "==",
                    "8.0.4"
                ]
            ]
        },
        {
            "name": "colorama",
            "specs": [
                [
                    "==",
                    "0.4.4"
                ]
            ]
        },
        {
            "name": "joblib",
            "specs": [
                [
                    "==",
                    "1.2.0"
                ]
            ]
        },
        {
            "name": "numpy",
            "specs": [
                [
                    "==",
                    "1.22.2"
                ]
            ]
        },
        {
            "name": "pydantic",
            "specs": [
                [
                    "==",
                    "1.9.0"
                ]
            ]
        },
        {
            "name": "tabulate",
            "specs": [
                [
                    "==",
                    "0.8.9"
                ]
            ]
        },
        {
            "name": "tqdm",
            "specs": [
                [
                    "==",
                    "4.64.1"
                ]
            ]
        },
        {
            "name": "typer",
            "specs": [
                [
                    "==",
                    "0.4.0"
                ]
            ]
        },
        {
            "name": "typing-extensions",
            "specs": [
                [
                    "==",
                    "4.1.1"
                ]
            ]
        }
    ],
    "lcname": "fuzzy-c-means"
}
Madson Dias
Elapsed time: 0.02075s