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Making Knowledge Extraction and Reasoning Closer

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Knowledge Discovery and Data Mining. Current Issues and New Applications (PAKDD 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1805))

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Abstract

The paper shows how a logic-based database language can support the various steps of the KDD process by providing a high degree of expressiveness, and the separation of concerns between the specification level and the mapping to the underlying databases and data mining tools. In particular, the mechanism of user-defined aggregates provided in LDL++ allows to specify data mining tasks and to formalize the mining results in a uniform way. We show how the mechanism applies to the concept of Inductive Databases, proposed in [2,12]. We concentrate on bayesian classification and show how user defined aggregates allow to specify the mining evaluation functions and the returned patterns. The resulting formalism provides a flexible way to customize, tune and reason on both the evaluation functions and the extracted knowledge.

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Author information

Authors and Affiliations

  1. CNUCE - CNR, Via S. Maria 36, 56125, Pisa, Italy

    Fosca Giannotti & Giuseppe Manco

Authors
  1. Fosca Giannotti
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  2. Giuseppe Manco
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Editor information

Editors and Affiliations

  1. Graduate School of Systems Management, Universiy of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan

    Takao Terano

  2. Department of Computer Science and Engineering, Arizona State University, P.O. Box 875 406, Tempe, AZ, 85287-5406

    Huan Liu

  3. Department of Computer Science, National Tsing Hua University, Hsinchu, 300, Taiwan ROC

    Arbee L. P. Chen

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© 2000 Springer-Verlag Berlin Heidelberg

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Giannotti, F., Manco, G. (2000). Making Knowledge Extraction and Reasoning Closer. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_42

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  • DOI: https://doi.org/10.1007/3-540-45571-X_42

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67382-8

  • Online ISBN: 978-3-540-45571-4

  • eBook Packages: Springer Book Archive

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