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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
    Rule Extraction from Time Series Databases using Classification Trees
    (: Citeseer, 2002-2) ;
    Due to the wide availability of huge data collection comprising multiple sequences that evolve over time, the process of adapting the classical data-mining techniques, making them capable to work into this new context, becomes today a strong necessity. Having as a final goal the extraction of temporal rules from time series databases, we proposed in this article a methodology permitting the application of a classification tree on sequential raw data by the use of a flexible approach of the main terms as ā€œclassification classā€, ā€œtraining setā€, ā€œattribute setā€, etc. We described also a first implementation of this methodology and presented some results on a synthetic time series database.