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Case studies: Public domain, multiple mining tasks systems: ROSETTA rough sets

Authors:

Jan Komorowski,

Aleksander Øhrn,

Andrzej SkowronAuthors Info & Claims

Handbook of data mining and knowledge discovery

January 2002

Pages 554 - 559

Published: 01 January 2002 Publication History

Abstract

Research in rough sets (Pawlak, 1981, 1982) has resulted in a number of software tools for data mining and knowledge discovery from databases (KDD). Among many of these tools, the ROSETTA system (Øhrn, 1999, Øhrn and Komorowski, 1997; Øhrn et al., 1998) is probably one of the most complete software environments for rough set operations. In ROSETTA, the experimental nature of inducing classifiers from data is explicitly maintained by organizing the workspace in a tree structure that displays how input and output data relate to each other. ROSETTA supports the overall KDD process: from browsing and preprocessing of the data, to reduct computation and rule synthesis, to validation and analysis of the generated rules. Learning may be both supervised (resulting in if-then rules) or unsupervised (resulting in general patterns), and input data may be categorical, numerical, or both. ROSETTA is not tied to any particular application domain, and it has been put to use for a variety of tasks. ROSETTA is a cooperative effort between researchers at NTNU in Norway and Warsaw University in Poland, and is available on the World Wide Web (http://www.idi.ntnu.no/ ~aleks/rosetta/). The system runs under Windows NT/98/95/2000.

References

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Nguyen, H. S., and S. H. Nguyen. 1998. "Discretization methods in data mining." In Rough Sets in Knowledge Discovery 1: Methodology and Applications, edited by L. Polkowski, and A. Skowron, pp. 451-482. Heidelberg: Physica-Verlag.

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Nguyen, S. H., and A. Skowron. 1997. "Searching for relational patterns in data." In Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD'97), edited by J. Komorowski and J. Zytkow, Lecture Notes in Artificial Intelligence 1263: 265-276. Trondheim, Norway, and Berlin: Springer-Verlag.

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Öhrn, A. 1999. "Discernibility and rough sets in medicine: tools and applications." Ph.D. diss. Norwegian University of Science and Technology. Available at: http://www.idi.ntnu.no/~aleks/thesis/.

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Öhrn, A., and J. Komorowski. 1997. "ROSETTA: a rough set toolkit for analysis of data." In Proceedings of the Third Joint Annual Conference on Information Sciences, Durham, NC, edited by P. P. Wang, pp. 403-407.

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Öhrn, A., J. Komorowski, A. Skowron, and P. Synak. 1998. "The design and implementation of a knowledge discovery toolkit based on rough sets: the ROSETTA system." In Rough Sets in Knowledge Discovery 1: Methodology and Applications, edited by L. Polkowski, and A. Skowron, pp. 376-399. Heidelberg: Physica-Verlag.

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Cited By

Janusz AStawicki SSzczuka MŚlęzak D(2015)Rough Set Tools for Practical Data ExplorationRough Sets and Knowledge Technology10.1007/978-3-319-25754-9_7(77-86)Online publication date: 20-Nov-2015
https://dl.acm.org/doi/10.1007/978-3-319-25754-9_7
Terlecki P(2010)On the relation between jumping emerging patterns and rough set theory with application to data classificationTransactions on rough sets XII10.5555/1880429.1880442(236-338)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.5555/1880429.1880442
Hvidsten TKomorowski J(2007)Rough sets in bioinformaticsTransactions on rough sets VII10.5555/1772666.1772681(225-243)Online publication date: 1-Jan-2007
https://dl.acm.org/doi/10.5555/1772666.1772681
Show More Cited By

Index Terms

Case studies: Public domain, multiple mining tasks systems: ROSETTA rough sets
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
  2. Information systems applications
    1. Data mining
2. Theory of computation
  1. Logic
    1. Type theory

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Information & Contributors

Information

Published In

cover image Guide books

Handbook of data mining and knowledge discovery

January 2002

1025 pages

ISBN:0195118316

Editors:
Willi Klösgen
Principal Researcher, Fraunhofer Institute for Autonomous Intelligent Systems, Sankt Augustin, Germany
,
Jan M. Zytkow
Deceased, formerly Professor of Computer Science, University of North Carolina at Charlotte

Publisher

Oxford University Press, Inc.

United States

Publication History

Published: 01 January 2002

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Cited By

Janusz AStawicki SSzczuka MŚlęzak D(2015)Rough Set Tools for Practical Data ExplorationRough Sets and Knowledge Technology10.1007/978-3-319-25754-9_7(77-86)Online publication date: 20-Nov-2015
https://dl.acm.org/doi/10.1007/978-3-319-25754-9_7
Terlecki P(2010)On the relation between jumping emerging patterns and rough set theory with application to data classificationTransactions on rough sets XII10.5555/1880429.1880442(236-338)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.5555/1880429.1880442
Hvidsten TKomorowski J(2007)Rough sets in bioinformaticsTransactions on rough sets VII10.5555/1772666.1772681(225-243)Online publication date: 1-Jan-2007
https://dl.acm.org/doi/10.5555/1772666.1772681
Rudnicki WKierczak MKoronacki JKomorowski J(2006)A statistical method for determining importance of variables in an information systemProceedings of the 5th international conference on Rough Sets and Current Trends in Computing10.1007/11908029_58(557-566)Online publication date: 6-Nov-2006
https://dl.acm.org/doi/10.1007/11908029_58
Bazan JSzczuka M(2005)The rough set exploration systemTransactions on Rough Sets III10.5555/2167525.2167528(37-56)Online publication date: 1-Jan-2005
https://dl.acm.org/doi/10.5555/2167525.2167528

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