Stochastic processes and temporal data mining

Authors:
Paul Cotofrei;Kilian Stoffel
Affiliations:
University of Neuchâtel;University of Neuchâtel
Venue:
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2007

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Abstract

This article tries to give an answer to a fundamental question intemporal data mining: "Under what conditions a temporal rule extracted from up-to-date temporal data keeps its confidence/support for future data". A possible solution is given by using, on the one hand, a temporal logic formalism which allows the definition of the main notions (event, temporal rule, support, confidence) in a formal way and, on the other hand, the stochastic limit theory. Under this probabilistic temporal framework, the equivalence between the existence of the support of a temporal rule and the law of large numbers is systematically analyzed.