afkmc2 0.1

Assumption Free and Efficient K-Means Seeding

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.

https://las.inf.ethz.ch/files/bachem16fast.pdf

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.

Usage

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_

Installation

Quickly install afkmc2 by running (coming soon):

pip install afkmc2

Contribute

• Issue Tracker: https://github.com/adriangoe/afkmc2/issues

Support

You can reach out to me through https://adriangoe.me/.

License

The project is licensed under the MIT License.