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Propositionalization Approaches to Relational Data Mining


Stefan Kramer, Nada Lavrac, and Peter Flach


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.



Contents


1.

Introduction


2.

Background and definition of terms


3.

An example illustrating a simple propositionalization


4.

Feature construction for general-purpose propositionalization


5.

Special-purpose feature construction


6.

Related transformation approaches


7.

A sample propositionalization method: Extending LINUS to handle non-determinate literals


7.

Concluding remarks


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