ROSE2 (Rough Sets Data Explorer) is a software implementing basic elements of the rough set theory and rule discovery techniques. It has been created at the Laboratory of Intelligent Decision Support Systems of the Institute of Computing Science in Poznan, basing on fourteen-year experience in rough set based knowledge discovery and decision analysis.
All computations are based on rough set fundamentals introduced by Z. Pawlak. One of implemented extensions applies the variable precision rough set model defined by W. Ziarko. It is particularly useful in analysis of data sets with large boundary regions. Another extension implements the rough set approach based on a similarity relation, as proposed by R. Slowinski. The similarity relation is assessed from data via inductive learning.
The ROSE2 system is a succesor of RoughDAS and RoughClass systems. RoughDAS is historically one of the first successful implementations of the rough set theory, which has been used in many real life applications.
The system contains several tools for rough set based knowledge discovery, e.g.:
- data preprocessing, including discretization of numerical attributes,
- performing a standard and an extended rough set based analysis of data,
- search of a core and reducts of attributes permitting data reduction,
- inducing sets of decision rules from rough approximations of decision classes,
- evaluating sets of rules in classifiaction experiments,
- using sets of decision rules as classifiers.