Assumption Free K-Means++ Seedings¶

Introduction¶

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 [afkmc2]

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:

pip install afkmc2

Contribute¶

Issue Tracker: https://github.com/adriangoe/afkmc2/issues
Source Code: https://github.com/adriangoe/afkmc2

Support¶

You can reach out to me through https://adriangoe.com/#contact-us.

License¶

The project is licensed under the MIT License.