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)

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.

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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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