Abstract
The analysis of time series databases is very important in the area of medicine. Most of the approaches that address this problem are based on numerical algorithms that calculate distances, clusters, index trees, etc. However, a symbolic rather than numerical analysis is sometimes needed to search for the characteristics of the time series. Symbolic information helps users to efficiently analyse and compare time series in the same or in a similar way as a domain expert would. This paper focuses on the process of transforming numerical time series into a symbolic domain and on the definition of both this domain and a distance for comparing symbolic temporal sequences. The work is applied to the isokinetics domain within an application called I4.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alonso, F., Martínez, L., Montes, C., Pérez, A., Santamaría, A., Valente, J.P.: Semantic Reference Model in Medical Time Series. In: Barreiro, J.M., Martín-Sánchez, F., Maojo, V., Sanz, F. (eds.) ISBMDA 2004. LNCS, vol. 3337, pp. 344–355. Springer, Heidelberg (2004)
Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequences of two proteins. J. Mol. Biol. 48, 443–453 (1970)
Alonso, F., Valente, J.P., Martínez, L., Montes, C.: Discovering Patterns and Reference Models in the Medical Domain of Isokinetics. In: Zurada, J.M. (ed.) New Generations of Data Mining Applications, IEEE Press/Wiley (2005)
Agrawal, R., Faloutsos, C., Swam, A.N.: Efficient Similarity Search In Sequence Databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time series databases. In: Proceedings of SIGMOD 1994, pp. 419–429. Minneapolis, MN (1994)
Rafei, D., Mendelzo, A.: Similarity-Based Queries for Time Series Data. In: Proceedings of SIGMOD, Arizona (1997)
Han, J., Dong, G., Yin, Y.: Efficient mining of partial periodic patterns in time series database. In: Proceedings of the 4th international conference on knowledge discovery and data mining, pp. 214–218. AAAI Press, Menlo Park (1998)
Agrawal, R., Psaila, G., Wimmers, E.L., Zaït, M.: Querying shapes of histories. IBM Research Report RJ 9962 (87921), IBM Almaden Research Center, San Jose,California (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alonso, F., Martínez, L., Pérez, A., Santamaría, A., Valente, J.P. (2006). Symbol Extraction Method and Symbolic Distance for Analysing Medical Time Series. In: Maglaveras, N., Chouvarda, I., Koutkias, V., Brause, R. (eds) Biological and Medical Data Analysis. ISBMDA 2006. Lecture Notes in Computer Science(), vol 4345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11946465_28
Download citation
DOI: https://doi.org/10.1007/11946465_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68063-5
Online ISBN: 978-3-540-68065-9
eBook Packages: Computer ScienceComputer Science (R0)