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fuzzy-c-means

Fuzzy c-means Clustering

Description

This implementation is based on the paper FCM: The fuzzy c-means clustering algorithm by: James C.Bezdek, Robert Ehrlich, and William Full

To run the tests

sh run_tests.sh

To run the coverage

sh run_coverage.sh

Install via pip

pip install fuzzycmeans

How to use it

  1. Fit the model. This is to cluster any given data X.
X = np.array([[1, 1], [1, 2], [2, 2], [0, 0], [0, 0]])
fcm = FCM(n_clusters=3, max_iter=1)
fcm.fit(X, [0, 0, 0, 1, 2])
  1. (Optional.) Use the model to assign new data points to existing clusters. Note that the predict function would return the membership as this a fuzzy clustering.
Y = np.array([[1, 2], [2, 2], [3, 1], [2, 1], [6, 8]])
membership = fcm.predict(Y)

Release files for fuzzycmeans 1.0.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for fuzzycmeans 1.0.4
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Built distribution (wheel)

Table of built distributions (wheels) for fuzzycmeans 1.0.4
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fuzzycmeans-1.0.4-py3-none-any.whl Python 3 none any Details

Total release size: 23.9 kB

Release files / fuzzycmeans-1.0.4.tar.gz

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