ROSETTA 1.00

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.

Some Features Currently Offered By The Computational Kernel Include:

• 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.
  
  
  • Utilities for statistical hypothesis testing.
  
  
  
  


• Miscellaneous
  
  
  
  • Clustering via tolerance relations.
  
  
  • Computation of partitions and variable precision rough set approximations.
  
  
  • Support for random sampling of observations.
  
  
  • Open source code.
  
  
  • Documentation.