Summary
Recently in different application domains, like medicine, industrial processes, etc., a great amount of data is recorded over a certain time period. Often quite complex patterns are searched among a background of irrelevant data, such that diagnoses becomes feasible. In the theory of formal languages complex temporal patterns are described by syntactic pattern recognition methods, where a grammatical inference is deduced. We propose to use extended context-free grammars, called Definitive Clause Grammars, to describe context dependence in primitive pattern sequences. Then we propose to use temporal expressions to build up the temporal patterns. It seems to us, that the ontological primitives usually used in AI-systems are too complex and too exact to real applications.
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
ALLEN, J. (1984): Towards a General Theory of Action and Time. Artificial Intelligence, 23, 123–154.
BISHOP, C. M. (1995): Neural Networks for Pattern Recognition, Clarendow Press, Oxford.
Bolc, L. (1987): Natural Language Parsing Systems. Springer Verlag.
BOW, T. (1984): Pattern Recognition, Marcel Dekker, New York.
DUDA, O. and HART, P. E. (1973): Pattern Classification and Scene Analysis, John Wiley and Sons, Inc. New York.
FU, S. (1982): Syntactic Pattern Recognition and Applications, Prentice-Hall, Englewood-Cliffs, N. J.
GONZALEZ, R. C. and THOMASON, M. G. (1978): Syntactic Pattern Recognition, Addison-Wesley.
KANAL, L. N. and DATTATREYA, G. R. (1992): Pattern Recognition. in S.C. Shapiro (ed): Encyclopedia of Artificial Intelligence, Vol. 2, John Wiley & Sons.
MCDERMOTT, D (1982): A Temporal Logic for Reasoning about Processes and Plans. Cognitive Science, 6, 101–155.
NAUMANN, S. and LANGER, H. (1994): Parsing: Eine Einführung in die maschinelle Analyse natürlicher Sprache, B.G. Teubner, Stuttgart.
PAO, Y.-H. (1994): Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, New York.
PEREIRA, F. and WARREN, D. (1980): Definitive Clause Grammars for Language Analysis — A Survey of the Formalism and a Comparison with Augmented Transition Networks. Artificial Intelligence 13, 231–278.
TOU, J. T. and GONZALEZ, R. C. (1974): Pattern Recognition Principles, Addison-Wesley.
SCHALKOFF, R. (1992): Pattern Recognition: Statistical, Structural and Neural Approaches, John Wiley & Sons, New York.
STERLING, L. and SHAPIRO, E. (1986): The Art of Prolog. MIT Press.
ULTSCH, A. and SIEMON, H. P. (1990): Kohonens Self Organizing Feature Maps for Exploratory Data Analysis, Proc. Intern. Neural Networks, Kluwer Academic Press, Paris, 305–308.
ULTSCH, A. (1991): Konnektionistische Modelle und ihre Integration mit wissensbasierten Systemen, Forschungsbericht Nr. 396, Institut für Informatik, Universität Dortmund, Februar, Dortmund, Habilitationsschrift.
VILA, L. (1994): A Survey on Temporal Reasoning in Artificial Intelligence. Ai Communications, Vol. 7, Nr.1, March, 4–28.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guimarães, G., Ultsch, A. (1997). A Symbolic Representation for Patterns in Time Series Using Definitive Clause Grammars. In: Klar, R., Opitz, O. (eds) Classification and Knowledge Organization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59051-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-59051-1_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-62981-8
Online ISBN: 978-3-642-59051-1
eBook Packages: Springer Book Archive