Title: Beam search induction and similarity constraints for predictive clustering trees
Authors: Kocev, Dragi ×
Struyf, Jan
Dzeroski, Saso #
Issue Date: 2007
Publisher: Springer
Host Document: Lecture notes in computer science vol:4747 pages:134-151
Conference: International workshop on Knowledge Discovery in Inductive Databases edition:5 location:Berlin, Germany date:September 18, 2006
Abstract: Much research on inductive databases (IDBs) focuses on local models, such as item sets and association rules. In this work, we investigate how IDBs can support global models, such as decision trees. Our focus is on predictive clustering trees (PCTs). PCTs generalize decision trees and can be used for prediction and clustering, two of the most common data mining tasks. Regular PCT induction builds PCTs top-down, using a greedy algorithm, similar to that of C4.5. We propose a new induction algorithm for PCTs based on beam search. This has three advantages over the regular method: (a) it returns a set of PCTs satisfying the user constraints instead of just one PCT; (b) it better allows for pushing of user constraints into the induction algorithm; and (c) it is less susceptible to myopia. In addition, we propose similarity constraints for PCTs, which improve the diversity of the resulting PCT set.
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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