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
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
kmc2 locally from this repository, you may use
pip install .
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
from sklearn.cluster import MiniBatchKMeans model = MiniBatchKMeans(5, init=seeding).fit(X) new_centers = model.cluster_centers_
Detailed Usage / API
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 Args: 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) Returns: (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 (email@example.com).
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
This research was partially supported by ERC StG 307036, a Google Ph.D. Fellowship and an IBM Ph.D. Fellowship.