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fuzzy-c-means
fuzzy-c-means
is a Python module implementing the Fuzzy C-means clustering algorithm.
instalation
the fuzzy-c-means
package is available in PyPI. 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
%matplotlib inline import numpy as np from fcmeans import FCM from matplotlib import pyplot as plt
creating artificial data set
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
fcm = FCM(n_clusters=2) fcm.fit(X)
showing results
# 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()
to more examples, see the 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
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
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