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Propositionalization Approaches to Relational Data Mining

Stefan Kramer, Nada Lavrac, Peter Flach, Propositionalization Approaches to Relational Data Mining. Chapter in Relational Data Mining. Saso Dzeroski, Nada Lavrac, (eds.). ISBN 3-540-42289-7, pp. 262–291. September 2001. No electronic version available. External information

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 domain-dependent 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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