Skip to content
Go to file

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time


Fast and Provably Good Seedings for k-Means using k-MC^2 and AFK-MC^2


The package provides a Cython implementation of the algorithms k-MC^2 and AFK-MC^2 described in the two papers:

Approximate K-Means++ in Sublinear Time. Olivier Bachem, Mario Lucic, S. Hamed Hassani and Andreas Krause. In Proc. Conference on Artificial Intelligence (AAAI), 2016.

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

The implementation is compatible with Python 2.7.


First make sure that numpy is installed by running

pip install numpy

The following command will then install kmc2 from PyPI:

pip install kmc2

To install kmc2 locally from this repository, you may use

pip install .


The kmc2 function may be used to run the algorithm and obtain a seeding. The data should be provided in a Numpy array or a Scipy CSR matrix.

import kmc2
X = <Numpy array containing the data>
seeding = kmc2.kmc2(X, 5)  # Run k-MC2 with k=5

The seeding can then be refined using MiniBatchKMeans of scikit-learn:

from sklearn.cluster import MiniBatchKMeans
model = MiniBatchKMeans(5, init=seeding).fit(X)
new_centers = model.cluster_centers_

Detailed Usage / API

The kmc2 module exposes a single function kmc2(...) with all the functionality:

def kmc2(X, k, chain_length=200, afkmc2=True, random_state=None, weights=None):
    """Cython implementation of k-MC2 and AFK-MC2 seeding

      X: (n,d)-shaped np.ndarray with data points (or scipy CSR matrix)
      k: number of cluster centers
      chain_length: length of the MCMC chain
      afkmc2: Whether to run AFK-MC2 (if True) or vanilla K-MC2 (if False)
      random_state: numpy.random.RandomState instance or integer to be used as seed
      weights: n-sized np.ndarray with weights of data points (default: uniform weights)

      (k, d)-shaped numpy.ndarray with cluster centers


To run the unittests, use nose in the package directory


Feedback / Citation

Please send any feedback to Olivier Bachem (

If you would like to cite this implementation, please reference the two original papers.


The software is released under the MIT License as detailed in kmeans.pyx.


This research was partially supported by ERC StG 307036, a Google Ph.D. Fellowship and an IBM Ph.D. Fellowship.

You can’t perform that action at this time.