General Features

ROSETTA is a toolkit for analyzing tabular data within the framework of rough set theory. ROSETTA is designed to support the overall data mining and knowledge discovery process: From initial browsing and preprocessing of the data, via computation of minimal attribute sets and generation of if-then rules or descriptive patterns, to validation and analysis of the induced rules or patterns.

ROSETTA is intended as a general-purpose tool for discernibility-based modelling, and is not geared specifically towards any particular application domain.

ROSETTA offers a highly intuitive GUI environment where data-navigational abilities are emphasized. The GUI is highly object-oriented in that all manipulable objects are represented as individual GUI items, each with their own set of context-sensitive menus.

The computational kernel is also available as a command-line program, suitable for being invoked from, e.g., Perl or Python scripts.



Some features currently offered by the computational kernel include:

ROSETTA offers an extensive array of alternative algorithms for most of the different options listed above, including algorithms from the RSES library.


Computational kernel and GUI front-end designed and implemented at the Knowledge Systems Group, Dept. of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway. Sections of the computational kernel (RSES) developed at the Group of Logic, Inst. of Mathematics, University of Warsaw, Poland.