TY - CHAP
T1 - Constraint programming for correlated itemset mining
AU - Guns, Tias
AU - Nijssen, Siegfried
AU - De Raedt, Luc
PY - 2009/12/1
Y1 - 2009/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84873823078&partnerID=8YFLogxK
M3 - Meeting abstract (Book)
AN - SCOPUS:84873823078
VL - 2
T3 - Belgian/Netherlands Artificial Intelligence Conference
SP - 315
EP - 316
BT - BNAIC 2009 Benelux Conference on Artificial Intelligence
T2 - 21st Benelux Conference on Artificial Intelligence, BNAIC 2009
Y2 - 29 October 2009 through 30 October 2009
ER -