Updated to meta docs.

Author: exhuma

CHANGELOG

@@ -1,4 +1,5 @@
 1.2.0
+   - Python 3 support
    - Split up one big file into smaller more logical sub-modules
 
 1.1.1b3

INSTALL

@@ -1,44 +1,27 @@
 INSTALLATION
 ============
 
-Linux
------
+Simply run::
 
-RPM-Installation
-~~~~~~~~~~~~~~~~
+    pip install cluster
 
-I'm not familiar with RPM-distributions but as far as I know it should be
-something like::
+Or, if you run it in a virtualenv:
 
-   rpm -i <filename.rpm>
+    /path/to/your/env/bin/pip install cluster
 
-RPM-source Installation
-~~~~~~~~~~~~~~~~~~~~~~~
 
-This is something I don't know. If somebody can enlighten me, please do!
+Source installation
+~~~~~~~~~~~~~~~~~~~
 
-Binary/Source installation
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+Untar the archive::
 
-Untar the package with you favourite archive tool. On the console it will be
-something along the lines::
-
-   tar xzf <filename.tar.gz>
+   tar xf <filename.tar.gz>
 
 Next, go to the folder just created. It will have the same name as the package
-(for example "cluster-1.0.0b1") and run::
-
-   python setup.py install
-
-For this step you need root-priviledges
-
-Windows
--------
+(for example "cluster-1.2.0") and run::
 
-Execute the executable file and follow the instructions displayed. Default
-values will be fine in most cases.
+    python setup.py install
 
-MacOS-X
--------
+This will require superuser privileges unless you install it in a virtual environment::
 
-Simply follow the same instructions as with the Linux-Source installation.
+    /path/to/your/env/bin/python setup.py install

README

@@ -9,13 +9,17 @@ between two of those objects. For simple datatypes, like integers, this can be
 as simple as a subtraction, but more complex calculations are possible. Right
 now, it is possible to generate the clusters using a hierarchical clustering
 and the popular K-Means algorithm. For the hierarchical algorithm there are
-different "linkage" (single, complete, average and uclus) methods available. I
-plan to implement other algoithms as well on an
-"as-needed" or "as-I-have-time" basis.
+different "linkage" (single, complete, average and uclus) methods available.
 
 Algorithms are based on the document found at
 http://www.elet.polimi.it/upload/matteucc/Clustering/tutorial_html/
 
+.. note::
+    The above site is no longer avaialble, but you can still view it in the
+    internet archive at:
+    https://web.archive.org/web/20070912040206/http://home.dei.polimi.it//matteucc/Clustering/tutorial_html/
+
+
 USAGE
 =====