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  • Publication
    MƩtadonnƩes seulement
    Temporal granular logic for temporal data mining
    In this article, a formalism for a specific temporal data mining task (the discovery of rules, inferred from databases of events having a temporal dimension), is defined. The proposed theoretical framework, based on first-order temporal logic, allows the definition of the main notions (event, temporal rule, constraint) in a formal way. This formalism is then extended to include the notion of temporal granularity and a detailed study is made to investigate the formal relationships between semantics for the same event in linear time structures with different granularities.
  • Publication
    MƩtadonnƩes seulement
    First-Order Logic Based Formalism for Temporal Data Mining
    (Berlin: Springer-Verlag, 2005) ;
    In this article we define a formalism for a methodology that has as purpose the discovery of knowledge, represented in the form of general Horn clauses, inferred from databases with a temporal dimension. To obtain what we called temporal rules, a discretisation phase that extracts events from raw data is applied first, followed by an induction phase, which constructs classification trees from these events. The theoretical framework we proposed, based on first-order temporal logic, permits us to define the main notions (event, temporal rule, constraint) in a formal way. The concept of consistent linear time structure allows us to introduce the notions of general interpretation and of confidence. These notions open the possibility to use statistical approaches in the design of algorithms for inferring higher order temporal rules, denoted temporal meta-rules.