Abstract
Temporal abstraction has been known as a powerful approach of data abstraction by converting temporal data into interval with abstracted values including trends and states. Most temporal abstraction methods, however, has been developed for regular temporal data, and they cannot be used when temporal data are collected irregularly. In this paper we introduced a temporal abstraction approach to irregular temporal data inspired from a real-life application of a large database in hepatitis domain.
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© 2003 Springer-Verlag Berlin Heidelberg
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Nguyen, T.D., Kawasaki, S., Ho, T.B. (2003). Discovery of Trends and States in Irregular Medical Temporal Data. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds) Discovery Science. DS 2003. Lecture Notes in Computer Science(), vol 2843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39644-4_40
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DOI: https://doi.org/10.1007/978-3-540-39644-4_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20293-6
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