Rule Extraction from Time Series Databases Using Classification Trees1

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.