ROSETTA

ROSETTA is a toolkit for analyzing tabular data within the framework of rough set theory. It is designed for supporting 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.

This toolkit is not specifically towards any particular application domain, it is intended as a general-purpose tool for discernibility-based modeling. Highly intuitive GUI environment is offered and in this environment data-navigational abilities are emphasized. The main orientation of GUI is representing all manipulable objects as individual GUI items, each with their own set of context-sensitive menus.

Rosetta toolkit has several features such as: import/export – partial integration with DBMSs via ODBC, exporting of rules, reducts, tables, graphs and other objects to various formats, including XML, C++ and Prolog; preprocessing – completion of decision tables with missing values, discretization of numerical attributes; computation – support for both unsupervised and supervised learning, support for user-defined notions of discernibility, efficient computation of exact or approximate reducts, for various types of discernibility.

Generation of if-then rules or descriptive patterns via reducts, execution of script files, support for cross-validation; postprocessing – advanced filtering of sets of reducts and rules; validation and analysis – application of synthesized rules to unseen examples, generation of confusion matrices.

ROC curves and calibration curves, evaluation of individual rules according to advanced measures of quality; miscellaneous – clustering via tolerance relations, computation of partitions and variable precision rough set approximations, support for random sampling of observations.