Symbol Extraction Method and Symbolic Distance for Analysing Medical Time Series
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.
KeywordsTime series characterization isokinetics symbolic distance information extraction and text mining
Unable to display preview. Download preview PDF.
- 3.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)Google Scholar
- 4.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)Google Scholar
- 5.Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time series databases. In: Proceedings of SIGMOD 1994, pp. 419–429. Minneapolis, MN (1994)Google Scholar
- 6.Rafei, D., Mendelzo, A.: Similarity-Based Queries for Time Series Data. In: Proceedings of SIGMOD, Arizona (1997)Google Scholar
- 7.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)Google Scholar
- 8.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)Google Scholar