Logic programming and databases
Logic programming and databases
Maintaining knowledge about temporal intervals
Communications of the ACM
A Framework for Temporal Data Mining
DEXA '98 Proceedings of the 9th International Conference on Database and Expert Systems Applications
Segmentation of Evolving Complex Data and Generation of Models
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
An introduction to symbolic data analysis and the SODAS software
Intelligent Data Analysis
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
Discovering Triggering Events from Longitudinal Data
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
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Longitudinal data consist of the repeated measurements of some variables which describe a process (or phenomenon) over time. They can be analyzed to unearth information on the dynamics of the process. In this paper we propose a temporal data mining framework to analyze these data and acquire knowledge, in the form of temporal patterns, on the events which can frequently trigger particular stages of the dynamic process. The application to a biomedical scenario is addressed. The goal is to analyze biosignal data in order to discover patterns of events, expressed in terms of breathing and cardiovascular system timeannotated disorders, which may trigger particular stages of the human central nervous system during sleep.