minorMerge branch 'master' of https://github.com/gittar/k-means-u-star

README.md

@@ -1,5 +1,5 @@
-# The k-means-u* clustering algorithm
-
+# The k-means-u* algorithm
+## non-local jumps and greedy retries improve k-means++ clustering
 ![GitHub Logo](notebooks/img/example.png)
 
 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. 
@@ -10,24 +10,24 @@ This repository contains example python code for the k-means-u and k-mean-u* alg
 * 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: `jupyter notebook algo-pure.ipynb`
+* 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 <br>
-  (a bare-bones implementation meant for easy understanding of the algorithms)
-* simu-detail.ipynb <br>
-  (detailed simulations and graphics to illustrate the way the algrithms work, uses kmeansu.py)
-* simu-bulk.ipynb <br>
-  (systematic simulations with various data sets to compare k-means-++, k-means-u and k-means-u*, uses kmeansu.py)
-* dataset_class.ipynb<br>
-  (examples for using the data generator)
+* [algo-pure.ipynb](https://github.com/gittar/k-means-u-star/blob/master/notebooks/algo-pure.ipynb)<br>
+  a bare-bones implementation meant for easy understanding of the algorithms
+* [simu-detail.ipynb](https://github.com/gittar/k-means-u-star/blob/master/notebooks/simu-detail.ipynb) <br>
+  detailed simulations and graphics to illustrate the way the algorithms work, uses kmeansu.py
+* [simu-bulk.ipynb](https://github.com/gittar/k-means-u-star/blob/master/notebooks/simu-bulk.ipynb) <br>
+  systematic simulations with various data sets to compare k-means-++, k-means-u and k-means-u*, uses kmeansu.py
+* [dataset_class.ipynb](https://github.com/gittar/k-means-u-star/blob/master/notebooks/dataset_class.ipynb)<br>
+  examples for using the data generator
 
 ## python files:
 * kmeansu.py <br>
   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 <br>
-  (contains a class "dataset" to generate test data sets and also an own implementation of k-means++ which allows to get  the codebook after initialization but before the run of k-means)
+  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 <br>
-  (various utility functions for plotting etc.)
+  various utility functions for plotting etc.