Constraint programming for correlated itemset mining

Tias Guns, Siegfried Nijssen, Luc De Raedt

Research output: Chapter in Book/Report/Conference proceedingMeeting abstract (Book)

Abstract

Discovering itemsets and conjunctive rules under constraints are popular topics in the data mining and machine learning communities, for which many algorithms have been proposed. Despite the abundance of research in this area, however, constraint programming (CP) techniques developed in the artificial intelligence community to deal with constraint satisfaction problems have never been applied to rule discovery. In [4], we show that CP can not only be applied in an intuitive, extendible way to rule discovery, but also that CP techniques significantly outperform existing approaches in data mining.

Original languageEnglish
Title of host publicationBNAIC 2009 Benelux Conference on Artificial Intelligence
Pages315-316
Number of pages2
Volume2
Publication statusPublished - 1 Dec 2009
Event21st Benelux Conference on Artificial Intelligence, BNAIC 2009 - Eindhoven, Netherlands
Duration: 29 Oct 200930 Oct 2009

Publication series

NameBelgian/Netherlands Artificial Intelligence Conference
ISSN (Print)1568-7805

Conference

Conference21st Benelux Conference on Artificial Intelligence, BNAIC 2009
Country/TerritoryNetherlands
CityEindhoven
Period29/10/0930/10/09

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