dense.cc version 1.0                      -*- coding: iso-8859-1 -*-

The dense.cc program finds dense itemsets, as described in the
publications

    Jouni K. Seppnen and Heikki Mannila. Dense Itemsets. In Ronny
    Kohavi, Johannes Gehrke, William DuMouchel, and Joydeep Ghosh,
    eds., Tenth ACM SIGKDD International Conference on Knowledge
    Discovery and Data Mining (KDD-2004), pp. 683-688, Seattle, WA,
    USA 2004.

and

    Jouni K. Seppnen. Using and extending itemsets in data mining:
    query approximation, dense itemsets, and tiles. Doctoral
    dissertation, Department of Computer Science and Engineering,
    Helsinki University of Technology, 2006.

The first publication is available (sadly, not open-access) at
http://portal.acm.org/citation.cfm?id=1014140 and the second at
http://lib.tkk.fi/Diss/2006/isbn951228202X/

The program is Copyright (C) 2005,2006 Jouni K. Seppnen, and is
distributed under the Boost Software License, Version 1.0. See the
accompanying file LICENSE. If you make use of the software when
composing a scientific publication, you should cite at least one of
the above publications; this is not a requirement of the license but
one of ethical scientific conduct.

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
LICENSE file for more details.

COMPILING AND USING
===================

To compile, edit the Makefile to ensure that the settings are
appropriate for your environment, and type "make". You need the 
Boost library from <http://www.boost.org/>.

To use, format your data as whitespace-separated words on newline-
separated lines; each line corresponds to a database tuple and each
word to an item that is present in the tuple. A small example data
file is distributed as example.dat.

On the command line, give first the name of the data file, then the
parameters sigma and delta; both are fractional numbers between 0 
and 1. Redirect the standard output to a file; progress will be
reported on standard error. In the output, each set is preceded
by its weak density as a fraction between 0 and 1.

