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    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.