Options
Cotofrei, Paul
Nom
Cotofrei, Paul
Affiliation principale
Fonction
Maître d'enseignement et de recherche
Email
paul.cotofrei@unine.ch
Identifiants
Résultat de la recherche
Voici les éléments 1 - 10 sur 15
- PublicationMétadonnées seulement
- PublicationMétadonnées seulement
- PublicationMétadonnées seulement
- PublicationMétadonnées seulement
- PublicationMétadonnées seulementTime Granularity in Temporal Data MiningIn this chapter, 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, confidence) 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 the support measures of the same event in linear time structures with different granularities. Finally, based on the concept of consistency, a strong result concerning the independence of the confidence measure for a temporal rule, over the worlds with different granularities, is proved.
- PublicationMétadonnées seulementStochastic Processes and Temporal Data Mining(2007-8)
; 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. - PublicationMétadonnées seulement
- PublicationMétadonnées seulementTemporal granular logic for temporal data miningIn 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.
- PublicationMétadonnées seulementFirst-Order Logic Based Formalism for Temporal Data MiningIn this article we define a formalism for a methodology that has as purpose the discovery of knowledge, represented in the form of general Horn clauses, inferred from databases with a temporal dimension. To obtain what we called temporal rules, a discretisation phase that extracts events from raw data is applied first, followed by an induction phase, which constructs classification trees from these events. The theoretical framework we proposed, based on first-order temporal logic, permits us to define the main notions (event, temporal rule, constraint) 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.
- PublicationMétadonnées seulement