This file describes DocumentCluster, a program for clustering text documents 
based on similarity of word frequencies. Document words are first filtered
against a specified stop word list, then stemmed using the classic Porter
stemming algorithm. The resulting data is then converted to Term Frequency -
Inverse Document Frequency values, and normalized so each document is a vector
of length one. The document data is internally represented as a sparse matrix
with collapsed word columns. The vectors are then clustered using the classic
k-means algorithm, using cosine similarity as the distance measure.

To install:
1. Copy files to a directory
2. Create a subdirectory 'data'
3. Move the supplied stopwords.txt file to this directory, or create a custom
version. Words within the file must be specified on a single line seperated by 
commas.
4. Compile source files
5. Copy the files to cluster to the data subdirectory
6. Run as DocumentCluster [number of clusters] [list of files]

The number of clusters will be the number specified or half the number of
files, whichever is less. Files that have no word overlap with other files 
after stop-word removal will be excluded from the clustering; this happens
rarely in practice.

    Copyright (C) 2013   Ezra Erb

This program is free software: you can redistribute it and/or modify it under 
the terms of the GNU General Public License version 3 as published by the Free 
Software Foundation.

This program is distributed in the hope that it will be useful, but WITHOUT 
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS 
FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with 
this program.  If not, see <http://www.gnu.org/licenses/>.

I'd appreciate a note if you find this program useful or make updates. Please 
contact me through LinkedIn or github (my profile also has a link to the code 
depository)