Metadata-Version: 2.1
Name: fuzzy-c-means
Version: 1.2.4
Summary: 
Home-page: https://github.com/omadson/fuzzy-c-means
License: MIT
Keywords: machine-learning,data-science,fuzzy-c-means,clustering
Author: Madson Dias
Author-email: madsonddias@gmail.com
Requires-Python: >=3.6,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Dist: jax (>=0.2.7,<0.3.0)
Requires-Dist: jaxlib (>=0.1.57,<0.2.0)
Project-URL: Repository, https://github.com/omadson/fuzzy-c-means
Description-Content-Type: text/markdown

# fuzzy-c-means

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

## instalation
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
```

## basic clustering example
simple example of use the `fuzzy-c-means` to cluster a dataset in two groups:

### importing libraries
```Python
%matplotlib inline
import numpy as np
from fcmeans import FCM
from matplotlib import pyplot as plt
```

### creating artificial data set
```Python
n_samples = 3000

X = np.concatenate((
    np.random.normal((-2, -2), size=(n_samples, 2)),
    np.random.normal((2, 2), size=(n_samples, 2))
))
```

### fitting the fuzzy-c-means
```Python
fcm = FCM(n_clusters=2)
fcm.fit(X)
```

### showing results
```Python
# outputs
fcm_centers = fcm.centers
fcm_labels = fcm.predict(X)

# plot result
f, axes = plt.subplots(1, 2, figsize=(11,5))
axes[0].scatter(X[:,0], X[:,1], alpha=.1)
axes[1].scatter(X[:,0], X[:,1], c=fcm_labels, alpha=.1)
axes[1].scatter(fcm_centers[:,0], fcm_centers[:,1], marker="+", s=500, c='w')
plt.savefig('images/basic-clustering-output.jpg')
plt.show()
```
<div align="center">
    <img src="examples/images/basic-clustering-output.jpg">
</div>

to more examples, see the [examples/](https://github.com/omadson/fuzzy-c-means/tree/master/examples) folder.


## how to cite fuzzy-c-means package
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)


## contributing

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

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/

