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International Symposium on Methodologies for Intelligent Systems

ISMIS 2003: Foundations of Intelligent Systems pp 402–407Cite as

Probability Logic Modeling of Knowledge Discovery in Databases

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2871))

Abstract

A Knowledge discovery in databases (KDD) system with probability deduction capability is expected to provide more information for decision making. Based on Bacchus probability logic and formal concept analysis, we propose a logic model for KDD with probability deduction. We use formal concept analysis within the semantics of probability logic to import the notion of concept into modeling of KDD. One of the most important features of a KDD system is its ability to discover previously unknown and potentially useful patterns. We formalize the definitions of previously unknown and potentially useful patterns.

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References

  1. Bacchus, F.: Representing and Reasoning With Probabilistic Knowledge. MIT Press, Cambridge (1990)

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  2. Papadimitriou, C.H.: Computational Complexity, pp. 87–91. MIT, Cambridge (1993)

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  3. Reichenbach, H.: Theory of Probability. University of California Press, Berkely (1949)

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  4. Deogun, J., Saquer, J.: Monotone Concepts for Formal Concept Analysis (2003) (preprint)

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  5. Wille, R.: Restructuring Lattice Theory: an Approach Based on Hierarchies of Concepts. In: Rival, I. (ed.) Ordered sets, pp. 445–470. Reidel, Dordecht (1982)

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  6. Ganter, B., Wille, R.: Formal Concept Analsis: Mathematical Foundations, Berlin (1999)

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, 68588-0115, U.S.A.

    Jitender Deogun & Liying Jiang

  2. The Center for Advanced Computer studies, University of Louisiana at Lafayette, Lafayette, LA, 70504-4330, U.S.A

    Ying Xie & Vijay Raghavan

Authors
  1. Jitender Deogun
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  2. Liying Jiang
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  3. Ying Xie
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  4. Vijay Raghavan
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Editor information

Editors and Affiliations

  1. The International WIC Institute, Beijing University of Technology, China

    Ning Zhong

  2. Department of Computer Science, University of North Carolina, NC 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Shimane University, 89-1 Enya-cho Izumo, 6938501, Shimane, Japan

    Shusaku Tsumoto

  4. Department of Informatics, Graduate School of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi, 819-0395, Fukuoka, Japan

    Einoshin Suzuki

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© 2003 Springer-Verlag Berlin Heidelberg

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Cite this paper

Deogun, J., Jiang, L., Xie, Y., Raghavan, V. (2003). Probability Logic Modeling of Knowledge Discovery in Databases. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science(), vol 2871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39592-8_56

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  • DOI: https://doi.org/10.1007/978-3-540-39592-8_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20256-1

  • Online ISBN: 978-3-540-39592-8

  • eBook Packages: Springer Book Archive

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