Abstract
Discovering the set of closed frequent patterns is one of the fundamental problems in Data Mining. Recent Constraint Programming (CP) approaches for declarative itemset mining have proven their usefulness and flexibility. But the wide use of reified constraints in current CP approaches leads to difficulties in coping with high dimensional datasets. In this paper, we propose the ClosedPattern global constraint to capture the closed frequent pattern mining problem without requiring reified constraints or extra variables. We present an algorithm to enforce domain consistency on ClosedPattern in polynomial time. The computational properties of this algorithm are analyzed and its practical effectiveness is experimentally evaluated.
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Notes
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For the sake of readability, our examples refer to items by their names instead of their indices.
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Value between \(\langle . \rangle \) indicates the frequency of a pattern.
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Lazaar, N. et al. (2016). A Global Constraint for Closed Frequent Pattern Mining. In: Rueher, M. (eds) Principles and Practice of Constraint Programming. CP 2016. Lecture Notes in Computer Science(), vol 9892. Springer, Cham. https://doi.org/10.1007/978-3-319-44953-1_22
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DOI: https://doi.org/10.1007/978-3-319-44953-1_22
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