fuzzy-c-means is a Python module implementing the Fuzzy C-means clustering algorithm.
the fuzzy-c-means package is available in PyPI. to install, simply type the following command:
pip install fuzzy-c-means
simple example of use the fuzzy-c-means to cluster a dataset in tree groups:
from fcmeans import FCM
from sklearn.datasets import make_blobs
from matplotlib import pyplot as plt
from seaborn import scatterplot as scatter
# create artifitial dataset
n_samples = 50000
n_bins = 3 # use 3 bins for calibration_curve as we have 3 clusters here
centers = [(-5, -5), (0, 0), (5, 5)]
X,_ = make_blobs(n_samples=n_samples, n_features=2, cluster_std=1.0,
centers=centers, shuffle=False, random_state=42)
# fit the fuzzy-c-means
fcm = FCM(n_clusters=3)
fcm.fit(X)
# outputs
fcm_centers = fcm.centers
fcm_labels = fcm.u.argmax(axis=1)
# plot result
%matplotlib inline
f, axes = plt.subplots(1, 2, figsize=(11,5))
scatter(X[:,0], X[:,1], ax=axes[0])
scatter(X[:,0], X[:,1], ax=axes[1], hue=fcm_labels)
scatter(fcm_centers[:,0], fcm_centers[:,1], ax=axes[1],marker="s",s=200)
plt.show()if you use fuzzy-c-means package in your paper, please cite it in your publication.
@misc{fuzzy-c-means,
author = "Madson Luiz Dantas Dias",
year = "2019",
title = "fuzzy-c-means: An implementation of Fuzzy $C$-means clustering algorithm.",
url = "https://github.com/omadson/fuzzy-c-means",
institution = "Federal University of Cear\'{a}, Department of Computer Science"
}
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!