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Abstrakty
Traditional data mining methods based on rough set theory focus on extracting models which are good at classifying unseen obj-ects. If one wants to uncover new knowledge from the data, the model must have a high descriptive quality-it must describe the data set in a clear and concise manner, without sacrificing classification performance. Rough modeling, introduced by Kowalczyk (1998), is an approach which aims at providing models with good predictive emphand descriptive qualities, in addition to being computationally simple enough to handle large data sets. As rough models are flexible in nature and simple to generate, it is possible to generate a large number of models and search through them for the best model. Initial experiments confirm that the drop in performance of rough models compared to models induced using traditional rough set methods is slight at worst, and the gain in descriptive quality is very large.
Słowa kluczowe
Wydawca
Rocznik
Tom
Numer
Strony
675-690
Opis fizyczny
Daty
wydano
2001
otrzymano
2001-03-01
poprawiono
2001-06-01
Twórcy
autor
- Research and Development, FAST Search and Transfer ASA, Stoperigata 2, P.O. Box 1677 Vika, N-0120 Oslo, Norway
autor
- Knowledge Systems Group, Department of Information and Computer Science, Norwegian University of Science and Technology, N-7491 Trondheim, Norway
Bibliografia
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- Ågotnes T., Komorowski J. and Lrken T. (1999b): Taming large rule models in rough set approaches, In: Principles of Data Mining and Knowledge Discovery (J.M. Zytkow and J. Rauch, Eds.). - Berlin: Springer-Verlag.
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- Carlin U., Komorowski J. and Rhrn A. (1998): Rough set analysis of patients with suspected acute appendicitis. - Proc. 7-th Conf. Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU'98, Paris, France,pp.1528-1533.
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- Greco S., Matarazzo B. and Słowinski R. (1998): New developments in the rough set approach to multi-attribute decision analysis. - Bull. Int. Rough Set Soc., Vol.2, No.23, pp.57-87.
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- hrn A., Komorowski J., Skowron A. and Synak P. (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. - Heidelberg: Physica-Verlag, Ch.19, pp.376-399.
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- Kohavi R. and Sommerfield D. (1995): Feature subset selection using the wrapper method: Overfitting and dynamic search space topology. - Proc. 1-st Int. Conf. Knowledge Discovery and Data Mining, KDD'95.
- Kowalczyk W. (1998): Rough data modelling: A new technique for analyzing data, In: Rough Sets and Knowledge Discovery 1: Methodology and Applications. - Heidelberg: Physica-Verlag, Ch.20, pp.400-421.
- Mienko R., Stefanowski J. and Vanderpooten D. (1996): Discovery-oriented induction of decision rules. - Tech. Rep., Cahier du Lamsade No. 141, Universite de Paris Dauphine.
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Bibliografia
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