Abstract
In recent years, a large number of algorithms have been proposed for finding set patterns in boolean 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. © 2010 Springer Science+Business Media, LLC.
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CITATION STYLE
Besson, J., Boulicaut, J. F., Guns, T., & Nijssen, S. (2010). Generalizing itemset mining in a constraint programming setting. In Inductive Databases and Constraint-Based Data Mining (pp. 107–126). Springer New York. https://doi.org/10.1007/978-1-4419-7738-0_5
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