This repository contains example python code for the k-means-u and k-mean-u* algorithms as proposed in https://arxiv.org/abs/1706.09059.
- clone repository:
git clone https://github.com/gittar/k-means-u-star - cd main directory:
cd k-means-u-star - install miniconda or anaconda: https://conda.io/docs/install/quick.html
- create kmus environment:
conda env create -f envsimple.yml - activate environment:
source activate kmus(on windows:activate kmus) - start one of the jupyter notebooks, e.g.:
jupyter notebook notebooks/algo-pure.ipynb - continue in the browser window which opens (jupyter manual: http://jupyter-notebook.readthedocs.io/en/latest/)
- algo-pure.ipynb
a bare-bones implementation meant for easy understanding of the algorithms - simu-detail.ipynb
detailed simulations and graphics to illustrate the way the algorithms work, uses kmeansu.py - simu-bulk.ipynb
systematic simulations with various data sets to compare k-means-++, k-means-u and k-means-u*, uses kmeansu.py - dataset_class.ipynb
examples for using the data generator
- kmeansu.py
main implementation of k-means-u and k-means-u*, makes heavy use of http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html for efficient implementations of k-means and k-means++, gathers certain statistics while training to enable systematic evaluation, code therefore a bit larger - bfdataset.py
contains a class "dataset" to generate test data sets and also an own implementation of k-means++ which allows to access the codebook after initialization but before the run of k-means - bfutil.py
various utility functions for plotting etc.