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Rule Extraction from Time Series Databases using Classification Trees
Maison d'édition
: Citeseer
Date de parution
2002-2
De la page
327
A la page
332
Résumé
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 “classification class”, “training set”, “attribute set”, etc. We described also a first implementation of this methodology and presented some results on a synthetic time series database.
Notes
, 2014
Nom de l'événement
IASTED International Conference on Applied Informatics
Lieu
Innsbruck
Identifiants
Type de publication
conference paper
