Cotofrei, Paul

2003, Cotofrei, Paul, Stoffel, Kilian, Sloot, Peter, Abramson, David, Bogdanov, Alexander, Dongarra, Jack, Zomaya, Albert, Gorbachev, Yuri

The theoretical framework we proposed, based on first-order temporal logic, permits to define the main notions used in temporal data mining (event, temporal rule) in a formal way. The concept of consistent linear time structure allows us to introduce the notions of general interpretation and of confidence. These notions open the possibility to use statistical approaches in the design of algorithms for inferring higher order temporal rules, denoted temporal meta-rules.

2002-4, Cotofrei, Paul, Stoffel, Kilian, Sloot, Peter, Tan, Chih Jeng Kenneth, Dongarra, Jack, Hoekstra, Alfons

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. In [1] we proposed a methodology permitting the application of a classification tree on sequential raw data and the extraction of the rules having a temporal dimension. In this article, we propose a formalism based on temporal first logic-order and we review the main steps of the methodology through this theoretical frame. Finally, we present some solutions for a practical implementation.