The Rough Sets framework has been used with success in several data mining
applications. Here we mention some of these:
RSES:
The RSES system [Syn] was developed in Poland and uses state
of the art techniques from the Rough Sets theory. The system is available for
Hewlett Packard work stations with or without a graphical interface. A
maximum of 30.000 objects with 16.000 attributes may be processed for
rule generation. The number of attributes poses few restraints, but
the relatively low number of maximal objects prevents it from being
used on large data sets.
Newer versions of RSES will have the possibility of scaling attribute values. This means that real valued attributes may be quantified into a number of partitions. It is also possible to join multiple attributes and represent them as one. This leads to a faster calculation of reducts.
In order to test generated rules, it is possible to split the database in two parts. The rule generation may then compute rules with basis on the first half, while testing of the classification ability of these rules is done on the other.
The system supports script programming in order to ease step-by-step
experimentation. Further, is it possible to choose between different
computing algorithms for finding minimal rules. This comes in handy
when the most thorough algorithm would take approximately 100 years
for large datasets.
DataLogic/R is a database ``mining'' from Reduct Systems Inc. The
software is based on theories of knowledge representation, inductive
logic and rough sets.
According to Reduct Systems, their software is unique
in that it analyzes logical patterns in data at different levels of
knowledge representation. This means that it can discover facts and
relationships not accessible with any other method, still according to
Reduct Systems.
The system is written in C to be easily portable. A packaged
version is available for PC, which works on a maximum number of 2000
attributes per object. It has been taken into commercial use within
finance.
Wall Street analyst Murray Riggiero Jr. used DataLogic in conjunction
with neural-network software to generate rules for his trading
system.
KDD-R:
This system is described in [ZS94], and is based on the Variable
Precision Rough Set (VPRS) model. It is implemented in C under UNIX. The
system features several units, of which the following are
mentioned here.
In the present form, the system also has a limited capability to
handle incomplete data and some incremental update capability.
LERS:
Learning from Examples based on Rough Sets, is another system
created for rule induction. The system handles inconsistencies in data
sets by following the principles of Rough Sets. These inconsistencies are not
corrected, but instead the upper and lower approximation for each
concept is calculated. Thereafter deterministic and indeterministic
rules are generated.
The operator of the system can choose whether the system shall operate
using methods for machine learning or knowledge acquisition. In the
first case one single minimal description is calculated which
distinguishes each concept from the others. In the second case all
rules on minimal form which can be derived from the given dataset is
calculated. In both cases the user has the choice between using the
local or global approximation.
Other Systems:
In addition to the aforementioned Rough Sets based systems, a number of
other systems based on the methodologies of Rough Sets were built in the
past. Best known are the
systems RoughDAS (Slowinski and Stefanowski, 1992), RSL (Gawrys and
Sienkiewicz, 1994) and GRG [SHC](Shan, Hamilton and Cercone,
1995).