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Propositionalization Approaches to Relational Data Mining

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Relational Data Mining

Abstract

This chapter surveys methods that transform a relational representation of a learning problem into a propositional (feature-based, attribute-value) representation. This kind of representation change is known as propositionalization. Taking such an approach, feature construction can be decoupled from model construction. It has been shown that in many relational data mining applications this can be done without loss of predictive performance. After reviewing both general-purpose and domaindependent propositionalization approaches from the literature, an extension to the Linus propositionalization method that overcomes the system’s earlier inability to deal with non-determinate local variables is described.

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Author information

Authors and Affiliations

  1. Machine Learning and Natural Language Processing Lab, Institute for Computer Science, Albert-Ludwigs University Freiburg, Am Flughafen 17, D-79110, Freiburg i. Br., Germany

    Stefan Kramer

  2. Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia

    Nada Lavrač

  3. Department of Computer Science, University of Bristol, The Merchant Venturers Building, Woodland Road, Bristol, BS8 1UB, UK

    Peter Flach

Authors
  1. Stefan Kramer
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  2. Nada Lavrač
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  3. Peter Flach
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Editor information

Editors and Affiliations

  1. Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia

    Sašo Džeroski  & Nada Lavrač  & 

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© 2001 Springer-Verlag Berlin Heidelberg

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Kramer, S., Lavrač, N., Flach, P. (2001). Propositionalization Approaches to Relational Data Mining. In: Džeroski, S., Lavrač, N. (eds) Relational Data Mining. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04599-2_11

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  • DOI: https://doi.org/10.1007/978-3-662-04599-2_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-07604-6

  • Online ISBN: 978-3-662-04599-2

  • eBook Packages: Springer Book Archive

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