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From Inductive Logic Programming to Relational Data Mining

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Logics in Artificial Intelligence (JELIA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4160))

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Abstract

Situated at the intersection of machine learning and logic programming, inductive logic programming (ILP) has been concerned with finding patterns expressed as logic programs. While ILP initially focussed on automated program synthesis from examples, it has recently expanded its scope to cover a whole range of data analysis tasks (classification, regression, clustering, association analysis). ILP algorithms can this be used to find patterns in relational data, i.e., for relational data mining (RDM). This paper briefly introduces the basic concepts of ILP and RDM and discusses some recent research trends in these areas.

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Author information

Authors and Affiliations

  1. Department of Knowledge Technologies, Jozef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenija

    Sašo Džeroski

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  1. Sašo Džeroski
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Liverpool, Liverpool, U.K.

    Michael Fisher

  2. University of Liverpool, United Kingdom

    Wiebe van der Hoek

  3. University of Liverpool, Liverpool, UK

    Boris Konev

  4. Department of Computer Science, The University of Liverpool,  

    Alexei Lisitsa

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© 2006 Springer-Verlag Berlin Heidelberg

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Džeroski, S. (2006). From Inductive Logic Programming to Relational Data Mining. In: Fisher, M., van der Hoek, W., Konev, B., Lisitsa, A. (eds) Logics in Artificial Intelligence. JELIA 2006. Lecture Notes in Computer Science(), vol 4160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11853886_1

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  • DOI: https://doi.org/10.1007/11853886_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-39625-3

  • Online ISBN: 978-3-540-39627-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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