Abstract
Longitudinal studies are observational studies that involve repeated observations of the same variables over long periods of time. In this paper, we propose the use of tree pattern mining techniques to discover potentially interesting patterns within longitudinal data sets. Following the approach described in [15], we propose four different representation schemes for longitudinal studies and we analyze the kinds of patterns that can be identified using each one of the proposed representation schemes. Our analysis provides some practical guidelines that might be useful in practice for exploring longitudinal datasets.
The work described in this paper has been partially supported by the TIN2009-08296 research project from the Spanish Ministry of Science and Innovation.
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Jiménez, A., Berzal, F., Cubero, JC. (2011). Mining Patterns from Longitudinal Studies. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25856-5_13
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DOI: https://doi.org/10.1007/978-3-642-25856-5_13
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