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The Variable Precision Rough Set Inductive Logic Programming Model and Web Usage Graphs

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New Frontiers in Artificial Intelligence (JSAI 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2253))

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Abstract

Inductive Logic Programming [42.1] is the research area formed at the intersection of logic programming and machine learning. Rough set theory [42.2], [42.3] defines an indiscernibility relation, where certain subsets of examples cannot be distinguished. The gRS-ILP model [42.4] introduces a rough setting in Inductive Logic Programming and describes the situation where the background knowledge, declarative bias and evidence are such that it is not possible to induce any logic program from them that is able to distinguish between certain positive and negative examples. Any induced logic program will either cover both the positive and the negative examples in the group, or not cover the group at all, with both the positive and the negative examples in this group being left out.

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Author information

Authors and Affiliations

  1. Anna University, 600 025, Chennai, India

    V. Uma Maheswari, Arul Siromoney & K. M. Mehata

Authors
  1. V. Uma Maheswari
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  2. Arul Siromoney
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  3. K. M. Mehata
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Editor information

Editors and Affiliations

  1. Graduate School of Business Science, The University of Tsukuba, Tokyo

    Takao Terano  & Yukio Ohsawa  & 

  2. School of Engineering, The University of Tokyo, Tokyo

    Toyoaki Nishida

  3. Dept.of Computer Science, National Defense Academy, Japan

    Akira Namatame

  4. School of Medicines, Shimane Medical University, Japan

    Syusaku Tsumoto

  5. The Institute of Scientific and Industrial Research, Osaka University, Osaka

    Takashi Washio

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© 2001 Springer-Verlag Berlin Heidelberg

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Uma Maheswari, V., Siromoney, A., Mehata, K.M. (2001). The Variable Precision Rough Set Inductive Logic Programming Model and Web Usage Graphs. In: Terano, T., Ohsawa, Y., Nishida, T., Namatame, A., Tsumoto, S., Washio, T. (eds) New Frontiers in Artificial Intelligence. JSAI 2001. Lecture Notes in Computer Science(), vol 2253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45548-5_42

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  • DOI: https://doi.org/10.1007/3-540-45548-5_42

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43070-4

  • Online ISBN: 978-3-540-45548-6

  • eBook Packages: Springer Book Archive

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