Time Granularity in Temporal Data Mining
Publisher
Berlin: Springer Verlag
Date issued
2009
In
Foundations of Computational Intelligence Volume 6: Data Mining
No
6
From page
67
To page
96
Serie
Studies in Computational Intelligence
Abstract
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.
Later version
http://link.springer.com/chapter/10.1007/978-3-642-01091-0_4
Publication type
book part