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Stochastic Processes and Temporal Data Mining
Date de parution
2007-8
De la page
183
A la page
190
Résumé
This article tries to give an answer to a fundamental question in temporal 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 analysed.
Notes
, 2007
Nom de l'événement
KDD-2007. 13th ACM SIGKDD International Confrerence on Knowledge Discovery and Data Mining
Lieu
San Jose, USA
Identifiants
Autre version
http://dl.acm.org/citation.cfm?id=1281215
Type de publication
conference paper
