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Propositionalization Approaches to Relational Data Mining

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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.

Keywords

  • Background Knowledge
  • Inductive Logic
  • Inductive Logic Programming
  • Feature Construction
  • Propositional Representation

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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

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