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Assumption Free and Efficient K-Means Seeding

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Assumption Free KMeans Monte Carlo

This package contains sklearn compatible python implementations of various K-Means seeding algorithms.

The package was inspired by the AFKMC^2 algorithm detailed in

Fast and Provably Good Seedings for k-Means
Olivier Bachem, Mario Lucic, S. Hamed Hassani and Andreas Krause
In Neural Information Processing Systems (NIPS), 2016.

The algorithm uses Monte Carlo Markov Chain to quickly find good seedings for KMeans and offers a runtime improvement over the common K-Means++ algorithm.


Using this package to get seedings for KMeans in sklearn is as simple as:

import afkmc2
X = np.array([[1, 2], [1, 4], [1, 0],
             [4, 2], [4, 4], [4, 0]])
seeds = afkmc2.afkmc2(X, 2)

from sklearn.custer import KMeans
model = KMeans(n_clusters=2, init=seeds).fit(X)
print model.cluster_centers_


Quickly install afkmc2 by running (coming soon):

pip install afkmc2



You can reach out to me through


The project is licensed under the MIT License.

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