RULE EXTRACTION FROM TIME SERIES DATABASES USING CLASSIFICATION TREES 1

Abstract

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. KEY WORDS: data mining, temporal reasoning, classification trees, C4.5, temporal rules