Cotofrei, Paul

2005-7, Cotofrei, Paul, Stoffel, Kilian

In this article, a formalism for a specific temporal data mining task (the discovery of rules, inferred from databases of events having a temporal dimension), is defined. The proposed theoretical framework, based on first-order temporal logic, allows the definition of the main notions (event, temporal rule, constraint) in a formal way. This formalism is then extended to include the notion of temporal granularity and a detailed study is made to investigate the formal relationships between semantics for the same event in linear time structures with different granularities.

2002-2, Cotofrei, Paul, Stoffel, Kilian

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.