Discovering Knowledge About Key Sequences for Indexing Time Series Cases in Medical Applications
Coping with time series cases is becoming an important issue in case based reasoning. This paper develops a knowledge discovery approach to discovering significant sequences for depicting symbolic time series cases. The input is a case library containing time series cases consisting of consecutive discrete patterns. The proposed approach is able to find from the given case library all qualified sequences that are non-redundant and indicative. A sequence as such is termed as a key sequence. It is shown that the key sequences discovered are highly usable in case characterization to capture important properties while ignoring random trivialities. The main idea is to transform an original (lengthy) time series into a more concise representation in terms of the detected occurrences of key sequences. Three alternate ways to develop case indexes based on key sequences are suggested. These indexes are simply vectors of numbers that are easily usable when matching two time series cases for case retrieval.
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- 1.Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations and systems approaches. AI Communications 7, 39–59 (1994)Google Scholar
- 2.Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the 11th International Conference on Data Engineering, pp. 3–14 (1995)Google Scholar
- 4.Bichindaritz, I., Conlon, E.: Temporal knowledge representation and organization for case-based reasoning. In: Proc. TIME 1996, pp. 152–159. IEEE Computer Society Press, Washington (1996)Google Scholar
- 5.Chan, K.P., Fu, A.W.: Efficient time series matching by wavelets. In: Proceedings of the International Conference on Data Engineering, pp. 126–133 (1999)Google Scholar
- 6.Garofalakis, M.N., Rajeev, R., Shim, K.: SPIRIT: Sequential pattern mining with regular expressing constraints. In: Proceedings of the 25th International Conference on Very Large Data bases, pp. 223–234 (1999)Google Scholar
- 10.Montani, S., et al.: Case-based retrieval to support the treatment of end stage renal failure patients. Artificial Intelligence in Medicine (in press)Google Scholar
- 13.Olsson, E., Funk, P., Xiong, N.: Fault diagnosis in industry using sensor readings and case-based reasoning. Journal of Intelligent & Fuzzy Systems 15, 41–46 (2004)Google Scholar
- 14.Perner, P.: Incremental learning of retrieval knowledge in a case-based reasoning system. In: Ashley, K.D., Bridge, D.G. (eds.) Proceedings of the International Conference on Case-Based Reasoning, pp. 422–436. Springer, Heidelberg (2003)Google Scholar
- 15.Salton, G.: Automatic information organization and retrieval. McGraw-Hill, New York (1968)Google Scholar
- 17.von Schéele, B.: Classification Systems for RSA, ETCO2 and other physiological parameters. PBM Stressmedicine, Technical Report, Heden 110, 82131 Bollnäs, Sweden (1999)Google Scholar
- 20.Wu, Y., Agrawal, D., Abbadi, A.E.: A comparison of DFT and DWT based similarity search in time series databases. In: Proceedings of the 9th ACM CIKM Conference on Information and Knowledge Management, McLean, VA, pp. 488–495 (2000)Google Scholar