Skip to content
Python Implementation of Assumption Free K-Means Seeding using Markov Chain Monte Carlo
Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
afkmc2
docs
.gitignore
LICENSE
README.rst
setup.cfg
setup.py

README.rst

Documentation Status

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 [afkmc2]_
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.

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

Support

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

License

The project is licensed under the MIT License.

You can’t perform that action at this time.