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Data Mining in Inductive Databases

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Knowledge Discovery in Inductive Databases (KDID 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3933))

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Abstract

Ever since the seminal paper by Imielinski and Mannila [11], inductive databases have been a constant theme in the data mining literature. Operationally, such an inductive database is a database in which models and patterns are first class citizens.

In the extensive literature on inductive databases there is at least one consequence of this operational definition that is conspicuously missing. That is the question: if we have models and patterns in our inductive database, how does this help to discover other models and patterns? This question is the topic of this paper.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Universiteit Utrecht, Padualaan 14, 3584CH, Utrecht, The Netherlands

    Arno Siebes

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  1. Arno Siebes
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Editors and Affiliations

  1. Pisa KDD Laboratory, ISTI - C.N.R, Area della Ricerca di Pisa, Via Giuseppe Moruzzi 1, Pisa, Italy

    Francesco Bonchi

  2. INSA-Lyon, LIRIS CNRS UMR5205, F-69621, Villeurbanne, France

    Jean-François Boulicaut

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© 2006 Springer-Verlag Berlin Heidelberg

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Siebes, A. (2006). Data Mining in Inductive Databases. In: Bonchi, F., Boulicaut, JF. (eds) Knowledge Discovery in Inductive Databases. KDID 2005. Lecture Notes in Computer Science, vol 3933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11733492_1

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  • DOI: https://doi.org/10.1007/11733492_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33292-3

  • Online ISBN: 978-3-540-33293-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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