Abstract
We propose a notion of deterministic association rules for ordered data. We prove that our proposed rules can be formally justified by a purely logical characterization, namely, a natural notion of empirical Horn approximation for ordered data which involves background Horn conditions; these ensure the consistency of the propositional theory obtained with the ordered context. The main proof resorts to a concept lattice model in the framework of Formal Concept Analysis, but adapted to ordered contexts. We also discuss a general method to mine these rules that can be easily incorporated into any algorithm for mining closed sequences, of which there are already some in the literature.
This work is supported in part by MCYT TIC 2002-04019-C03-01 (MOISES).
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Balcázar, J.L., Casas-Garriga, G. (2004). On Horn Axiomatizations for Sequential Data. In: Eiter, T., Libkin, L. (eds) Database Theory - ICDT 2005. ICDT 2005. Lecture Notes in Computer Science, vol 3363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30570-5_15
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DOI: https://doi.org/10.1007/978-3-540-30570-5_15
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