Jupyter Notebook Python
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The k-means-u* algorithm

non-local jumps and greedy retries improve k-means++ clustering

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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.

Quick Start

  • 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/)

jupyter notebooks:

  • 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

python files:

  • 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.