A Temporal Data Mining Approach for Discovering Knowledge on the Changes of the Patient's Physiology
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Discovering explanations from longitudinal data
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Mining Physiological Data for Discovering Temporal Patterns on Disease Stages
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
A temporal data mining framework for analyzing longitudinal data
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
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The problem of time-series segmentation has been widely discussed and it has been successfully applied in a variety of areas including computational genomics, telecommunications and process monitoring. Nevertheless not many techniques have been devised to deal with multidimensional evolving data describing complex objects. Moreover, in many applications the resulting segments have not a description understandable to the user, and this is exacerbated in the applications with complex data. Our contribute aims to propose an algorithmic framework to segment multidimensional evolving data or multidimensional time-series and to resort to an ILP system to generate characterizations of segments close to the user. The application and the results to the realworld data are reported.