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New Approach on Temporal Data Mining for Symbolic Time Sequences: Temporal Tree Associate Rules

Matthieu Guillame-Bert 1 James L. Crowley 1, *
* Corresponding author
1 PRIMA - Perception, recognition and integration for observation of activity
CNRS - Centre National de la Recherche Scientifique : UMR5217, INPG - Institut National Polytechnique de Grenoble , UJF - Université Joseph Fourier - Grenoble 1, Inria Grenoble - Rhône-Alpes
Abstract : We introduce a temporal pattern model called Temporal Tree Associative Rule (TTA rule). This pattern model can be used to express both uncertainty and temporal inaccuracy of temporal events expressed as Symbolic Time Sequences. Among other things, TTA rules can express the usual time point operators, synchronicity, order, chaining, as well as temporal negation. TTA rule is designed to allows predictions with optimum temporal precision. Using this representation, we present an algorithm that can be used to extract Temporal Tree Associative rules from large data sets of symbolic time sequences. This algorithm is a mining heuristic based on entropy maximisation and statistical independence analysis. We discuss the evaluation of probabilistic temporal rules, evaluate our technique with an experiment and discuss the results.
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Submitted on : Thursday, January 10, 2013 - 12:07:57 PM
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Matthieu Guillame-Bert, James L. Crowley. New Approach on Temporal Data Mining for Symbolic Time Sequences: Temporal Tree Associate Rules. ICTAI 2011 - International Conference on Tools with Artificial Intelligence, Nov 2011, Boca Raton, FL, United States. pp.748-752, ⟨10.1109/ICTAI.2011.117⟩. ⟨hal-00772269⟩



Les métriques sont temporairement indisponibles