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Knowledge Discovery by Application of Rough Set Models

  • Chapter
Rough Set Methods and Applications

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 56))

Abstract

The amount of electronic data available is growing very fast and this explosive growth in databases has generated a need for new techniques and tools that can intelligently and automatically extract implicit, previously unknown, hidden and potentially useful information and knowledge from these data. These tools and techniques are the subject of the field of Knowledge Discovery in Databases. In this Chapter we discuss selected rough set based solutions to two main knowledge discovery problems, namely the description problem and the classification (prediction) problem.

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  1. Institute of Computer Science Bialystok, University of Technology, Wiejska 45A, 15-351, Bialystok, Poland

    Jaroslaw Stepaniuk

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Editors and Affiliations

  1. Department of Mathematics and Information Sciences, Warsaw University of Technology, Pl. Politechnik 1, 00650, Warsaw, Poland

    Lech Polkowski

  2. Department of Medical Informatics, Shinmane Medical University, 89-1 Enya-cho, 693-8501, Izumo City, Shimane, Japan

    Shusaku Tsumoto

  3. Department of Mathematics and Computer Science, San Jose State University, San Jose, CA, 95192, USA

    Tsau Y. Lin

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Stepaniuk, J. (2000). Knowledge Discovery by Application of Rough Set Models. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds) Rough Set Methods and Applications. Studies in Fuzziness and Soft Computing, vol 56. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1840-6_5

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