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User's Constraints in Itemset Mining

Christian Bessière 1 Nadjib Lazaar 1 Mehdi Maamar 2
1 COCONUT - Agents, Apprentissage, Contraintes
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Discovering significant itemsets is one of the fundamental tasks in data mining. It has recently been shown that constraint programming is a flexible way to tackle data mining tasks. With a constraint programming approach, we can easily express and efficiently answer queries with user’s constraints on itemsets. However, in many practical cases queries also involve user’s constraints on the dataset itself. For instance, in a dataset of purchases, the user may want to know which itemset is frequent and the day at which it is frequent. This paper presents a general constraint programming model able to handle any kind of query on the dataset for itemset mining.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01896872
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Submitted on : Wednesday, October 17, 2018 - 5:12:45 PM
Last modification on : Monday, July 27, 2020 - 10:32:02 AM
Long-term archiving on: : Friday, January 18, 2019 - 3:21:13 PM

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Christian Bessière, Nadjib Lazaar, Mehdi Maamar. User's Constraints in Itemset Mining. CP: Principles and Practice of Constraint Programming, Aug 2018, Lille, France. pp.537-553, ⟨10.1007/978-3-319-98334-9_35⟩. ⟨lirmm-01896872⟩

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