Title: Integrating decision tree learning into inductive databases
Authors: Fromont, Elisa ×
Blockeel, Hendrik
Struyf, Jan #
Issue Date: 2007
Publisher: Springer
Host Document: Lecture notes in computer science vol:4747 pages:81-96
Conference: International Workshop on Knowledge Discovery in Inductive Databases edition:5 location:Berlin, Germany date:September 18, 2006
Abstract: In inductive databases, there is no conceptual difference between data and the models describing the data: both can be stored and queried using some query language. The approach that adheres most strictly to this philosophy is probably the one proposed by Calders et al. (2006): in this approach, models are stored in relational tables and queried using standard SQL. The approach has been described in detail for association rule discovery. In this work, we study how decision tree induction can be integrated in this approach. We propose a representation format for decision trees similar to the format proposed earlier for association rules, and queryable using standard SQL; and we present a prototype system in which part of the needed functionality is implemented. In particular, we have developed an exhaustive tree learning algorithm able to answer a wide range of constrained queries.
ISBN: 978-3-540-75548-7
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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