Abstract
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 time-annotated disorders, which may trigger particular stages of the human central nervous system during sleep.
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References
Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)
Ceri, S., Gottlob, G., Tanca, L.: Logic Programming and Databases. Springer, Heidelberg (1990)
Chen, X., Petrounias, I.: A framework for temporal data mining. In: Quirchmayr, G., Bench-Capon, T.J.M., Schweighofer, E. (eds.) DEXA 1998. LNCS, vol. 1460, pp. 796–805. Springer, Heidelberg (1998)
Diday, E., Esposito, F.: An introduction to symbolic data analysis and the sodas software. Intell. Data Anal. 7(6), 583–601 (2003)
Loglisci, C., Berardi, M.: Segmentation of evolving complex data and generation of models. In: ICDM Workshops, pp. 269–273. IEEE Computer Society, Los Alamitos (2006)
Loglisci, C., Malerba, D.: Discovering triggering events from longitudinal data. In: ICDM Workshops, pp. 248–256. IEEE Computer Society Press, Los Alamitos (2008)
Malerba, D., Lisi, F.A.: An ILP method for spatial association rule mining. In: First Workshop on Multi-Relational Data Mining, pp. 18–29 (2001)
Mörchen, F.: Unsupervised pattern mining from symbolic temporal data. SIGKDD Explorations 9(1), 41–55 (2007)
Muggleton, S.: Inductive Logic Programming. Academic Press, London (1992)
Singer, J.D., Willet, J.B.: Applied longitudinal data analysis. Modelling change and event occurrence. Oxford University Press, Inc., Oxford (2003)
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Loglisci, C., Ceci, M., Malerba, D. (2011). A Temporal Data Mining Framework for Analyzing Longitudinal Data. In: Hameurlain, A., Liddle, S.W., Schewe, KD., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2011. Lecture Notes in Computer Science, vol 6861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23091-2_9
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DOI: https://doi.org/10.1007/978-3-642-23091-2_9
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