On Horn Axiomatizations for Sequential Data

  • José L. Balcázar
  • Gemma Casas-Garriga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3363)


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.


Association Rule Input Sequence Closure Operator Minimal Generator Concept Lattice 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • José L. Balcázar
    • 1
  • Gemma Casas-Garriga
    • 1
  1. 1.Departament de Llenguatges i Sistemes InformàticsUniversitat Politècnica de Catalunya 

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