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Artificial Intelligence in Medicine
Volume 39, Issue 1
, Pages 25-47
, January 2007
Learning recurrent behaviors from heterogeneous multivariate time-series
References
- Antunes C, Oliveira A. Temporal data mining: an overview. In: Proceedings of the workshop on temporal data mining at the 7th international conference on knowledge discovery and data mining (KDD’01); 2001. p. 1–13.
- . A survey of temporal knowledge discovery paradigms and methods. IEEE Trans Knowl Data Eng. 2002;14(4):750–767
- . Feature-based classification of time-series data. Int J Comput Res, Special Issue: Inform Process Technol. 2001;10(3):49–61
- Hong P, Huang T. Learning to extract multi-temporal signal patterns from a temporal signal sequence. In: Proceedings of the 15th international conference on pattern recognition (ICPR’00), vol. 2. IEEE Press; 2000. p. 648–51.
- . Rule discovery from time series. In: Agrawal R, Stolorz P, Piatetsky-Shapiro G editor. Proceedings of the 4th international conference on knowledge discovery and data mining (KDD’98). Menlo Park, CA, USA: AAAI Press; 1998;p. 16–22
- . Discovery of temporal patterns—learning rules about the qualitative behaviour of time series. In: de Raedt L, Siebes A editor. Proceedings of the 5th European conference on principles and practice of knowledge discovery in databases (PKDD’01). Heidelberg, DE: Springer-Verlag; 2001;p. 192–203
- . An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In: Agrawal R, Stolorz P, Piatetsky-Shapiro G editor. Proceedings of the 4th international conference on knowledge discovery and data mining (KDD’98). New York, NY, USA: ACM Press; 1998;p. 239–241
- . Discovering similar multidimensional trajectories. In: Proceedings of the 18th international conference on data engineering (ICDE’02). Piscataway, NJ, USA: IEEE Press; 2002;p. 673–684
- . Health “smart” home: information technology for patients at home. Telemed J E-Health. 2002;8(4):395–410
- . A smart room for hospitalised elderly people: essay of modelling and first steps of an experiment. Technol Health Care. 1999;7(5):343–357
- . Fast similarity search in the presence of noise, scaling and translation in time-series databases. In: Dayal U, Gray PMD, Nishio S editor. Proceedings of the 21st international conference on very large data bases (VLDB’95). Morgan Kaufmann. 1995;p. 490–501
- . Similarity search for multidimensional data sequences. In: Proceedings of the 16th IEEE international conference on data engineering (ICDE’00). IEEE Press; 2000;p. 599–608
- . Probabilistic discovery of time series motifs. In: Getoor L, Senator T, Domingos P, Faloutsos C editor. Proceedings of the 9th ACM international conference on knowledge discovery and data mining (KDD’03). New York, NY, USA: ACM Press; 2003;p. 493–498
- . A new scheme for extracting multi-temporal sequence patterns. In: Proceedings of the international joint conference on neural networks (IJCNN’99), vol. 4. IEEE Press; 1999;p. 2643–2648
- . Finding motifs using random projections. J Comput Biol. 2002;9(2):225–242
- . Toward a portable blood pressure recorder device equipped with accelerometers. Med Eng Phys. 1999;21:343–352
- Duchêne F, Garbay C, Rialle V. An hybrid knowledge-based methodology for multivariate simulation in home health telecare. In: Proceedings of the joint workshop intelligent data analysis in medicine and pharmacology (IDAMAP) of the 9th artificial intelligence in medicine Europe conference (AIME); 2003. p. 87–94.
- . Similarity measure for heterogeneous multivariate time-series. In: Proceedings of the 12th European signal processing conference (EUSIPCO), EURASIP. 2004;p. 1605–1608
- Duchêne F. Mining heterogeneous multivariate time-series for learning meaningful patterns: application to home health telecare. Research Report 1070-I. Institut d’Informatique et Mathématiques Appliquées de Grenoble (IMAG), France; 2004. http://hal.ccsd.cnrs.fr/ccsd-00003350 [accessed: 15 May 2006].
- . Scalable feature mining for sequential data. IEEE Intell Syst. 2000;15(2):48–56
- . A monothetic clustering method. Pattern Recog Lett. 1998;19:989–996
- Lin J, Keogh E, Patel P, Lonardi S. Finding motifs in time series. In: Proceedings of the 2nd workshop on temporal data mining, at the 8th international conference on knowledge discovery and data mining (KDD’02), 2002. p. 53–68.
- Monod H, Pottier M. Adaptations respiratories et circulatories du travail musculaire. In: Scherrer J, editor. Précis de physiologie du travail, notions d’ergonomie, 2nd ed. (masson edition). Paris; 1981. p. 159–204.
- . Modified gath-geva clustering for fuzzy segmentation of multivariate time-series, fuzzy sets and systems. Data Mining Special Issue. 2005;149(1):39–56
- A. Gionis, H. Mannila, E. Terzi, Clustered segmentations. In: Workshop on mining temporal and sequential data (TDM), 10th ACM SIGKDD international conference on knowledge discovery and Data Mining (KDD’04); 2004.
- . Feature generation for sequence categorization. In: Proceedings of the 15th national conference on artificial intelligence (AAAI’98). Menlo Park, CA, USA: AAAI Press; 1998;p. 733–739
- . Finding similar time series. Principles Data Mining Knowl Discov. 1997;19:88–100
PII: S0933-3657(06)00102-3
doi: 10.1016/j.artmed.2006.07.004
© 2006 Elsevier B.V. All rights reserved.
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Artificial Intelligence in Medicine
Volume 39, Issue 1
, Pages 25-47
, January 2007
