The design and implementation of a knowledge discovery toolkit based on rough set -- the ROSETTA system. The KDD process using rough sets has been presented and analyzed. Following the requirement specifications of a sophisticated user-environment for empirical model construction, the design and implementation of a software toolkit has been outlined. The resulting toolkit covers the whole range of KDD tasks within the realm of rough sets. It consists of a general C++ class library primarily aimed at researchers for rapid prototyping, and a GUI front-end developed for knowledge discovery in an interactive setting. Issues springing from the overall KDD process have been the principal guiding design parameters for both.par The kernel class library provides a set of fundamental building blocks and the means to combine these in a flexible fashion, both for the development and testing of new algorithms and for partial automation of the overall KDD process. Various design choices made during construction of the class library have been outlined, and examples of its use been given. The GUI offers an environment wherein the fundamental tools furnished by the kernel are set. This enables interactive manipulation and creation of objects related to the KDD process. Jointly, the kernel and the front-end offer a means to effectively and easily conduct KD and data mining experiments within the framework of rough set theory.par In a few aspects, the work on ROSETTA is related to our previous research on providing tools and programming environments for process-oriented synthesis of logic programs.

References in zbMATH (referenced in 23 articles )

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  1. Amin, Talha; Moshkov, Mikhail: Totally optimal decision rules (2018)
  2. Beer, Frank; Bühler, Ulrich: In-database feature selection using rough set theory (2016)
  3. Jiang, Feng; Sui, Yuefei; Zhou, Lin: A relative decision entropy-based feature selection approach (2015)
  4. Lazo-Cortés, Manuel S.; Martínez-Trinidad, José Fco.; Carrasco-Ochoa, Jesús A.; Sanchez-Diaz, Guillermo: On the relation between rough set reducts and typical testors (2015)
  5. Nandhini, M.; Sivanandam, S. N.: An improved predictive association rule based classifier using gain ratio and T-test for health care data diagnosis (2015) ioport
  6. Janusz, Andrzej: Algorithms for similarity relation learning from high dimensional data (2014)
  7. Gao, Can; Pedrycz, Witold; Miao, Duoqian: Rough subspace-based clustering ensemble for categorical data (2013)
  8. Miao, Duoqian; Gao, Can; Zhang, Nan; Zhang, Zhifei: Diverse reduct subspaces based co-training for partially labeled data (2011) ioport
  9. Shen, Yongjun; Li, Tianrui; Hermans, Elke; Ruan, Da; Wets, Geert; Vanhoof, Koen; Brijs, Tom: A hybrid system of neural networks and rough sets for road safety performance indicators (2010) ioport
  10. Avron, A.; Konikowska, B.: Rough sets and 3-valued logics (2008)
  11. Pawlak, Zdzisław; Skowron, Andrzej: Rough sets and Boolean reasoning (2007)
  12. Qu, Binbin; Lu, Yansheng; Xiao, Bing: A rule induction algorithm for incomplete information system (2006)
  13. Bazan, Jan G.; Szczuka, Marcin: The rough set exploration system (2005)
  14. Laplante, Phillip A.; Neill, Colin J.: Modeling uncertainty in software engineering using rough sets (2005) ioport
  15. Bazan, Jan G.; Szczuka, Marcin S.; Wojna, Arkadiusz; Wojnarski, Marcin: On the evolution of rough set exploration system (2004)
  16. Bazan, Jan G.; Szczuka, Marcin S.; Wróblewski, Jakub: A new version of rough set exploration system (2002)
  17. Mak, Brenda; Munakata, Toshinori: Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3 (2002)
  18. Vitória, Aida; Małuszyński, Jan: A logic programming framework for rough sets (2002)
  19. Bazan, Jan G.; Nguyen, Hung Son; Nguyen, Sinh Hoa; Synak, Piotr; Wróblewski, Jakub: Rough set algorithms in classification problem (2001)
  20. Bazan, Jan G.; Szczuka, Marcin: RSES and RSESlib -- A collection of tools for rough set computations (2001)

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