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Time Granularity in Temporal Data Mining
Maison d'édition
Berlin: Springer Verlag
Date de parution
2009
In
Foundations of Computational Intelligence Volume 6: Data Mining
No
6
De la page
67
A la page
96
Collection
Studies in Computational Intelligence
Résumé
In 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.
(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.
Identifiants
Autre version
http://link.springer.com/chapter/10.1007/978-3-642-01091-0_4
Type de publication
book part
