Generalizing Itemset Mining in a Constraint Programming Setting - Archive ouverte HAL
Chapitre D'ouvrage Année : 2010

Generalizing Itemset Mining in a Constraint Programming Setting

Résumé

In recent years, a large number of algorithms have been proposed for finding set patterns in binary data. This includes popular mining tasks based on, for instance, frequent (closed) itemsets. In this chapter, we develop a common framework in which these algorithms can be studied thanks to the principles of constraint programming. We show how such principles can be applied both in specialized and general solvers.
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Dates et versions

hal-01381543 , version 1 (14-10-2016)

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  • HAL Id : hal-01381543 , version 1

Citer

Jérémy Besson, Jean-François Boulicaut, Tias Guns, Siegfried Nijssen. Generalizing Itemset Mining in a Constraint Programming Setting. S. Dzeroski, B. Goethals, and P. Panov. Inductive Databases and Constraint-Based Data Mining, Springer, pp.107-126, 2010. ⟨hal-01381543⟩
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