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Outlier Mining in Rule-Based Knowledge Bases

  • Conference paper
Rough Sets and Current Trends in Computing (RSCTC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7413))

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Abstract

The paper presents the problem of outlier detection in rule-based knowledge bases. Unusual (rare) rules, regarded here as deviation, should be the subject of experts’ and knowledge engineers’ analysis because they allow influencing on the efficiency of inference in decision support systems. A different approaches to find outliers and the results of the experiments are presented.

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© 2012 Springer-Verlag Berlin Heidelberg

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Nowak-Brzezińska, A. (2012). Outlier Mining in Rule-Based Knowledge Bases. In: Yao, J., et al. Rough Sets and Current Trends in Computing. RSCTC 2012. Lecture Notes in Computer Science(), vol 7413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32115-3_24

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  • DOI: https://doi.org/10.1007/978-3-642-32115-3_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32114-6

  • Online ISBN: 978-3-642-32115-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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