Abstract
One of the main unresolved problems in data mining is related with the treatment of data that is inherently sequential. Algorithms for the inference of association rules that manipulate sequential data have been proposed and used to some extent but are ineffective, in some cases, because too many candidate rules are extracted and filtering the relevant ones is difficult and inefficient. In this work, we present a method and algorithm for the inference of sequential association rules that uses context-free grammars to guide the discovery process, in order to filter, in an efficient and effective way, the associations discovered by the algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Allen, J.: Natural Languages Understanding, 2ndedition. The Benjamin/Cummings Publishing Company, Redwood City (1995)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In Proceedings of the International Conference on Very Large Databases (1994) 487–499
Agrawal, R., Srikant, R.: Mining sequential patterns. In Proceedings of the International Conference on Data Engineering (1995) 3–14
Das, G., Mannila, H., Smyth, P.: Rule Discovery from Time Series. In Proceedings of Knowledge Discovery in Databases (1998) 16–22
Fama, E.. Efficient Capital Markets: a review of theory and empirical work. Journal of Finance (1970) 383–417
Garofalakis, M., Rastogi, R., Shim, K.: SPIRIT: Sequential Pattern Mining with Regular Expression Constraint. In Proceedings of the International Conference on Very Large Databases (1999). 223–234
Grossman, R., Kamath, C, Kegelmeyer, P., Kumar, V., Namburu, R.: Data Mining for Scientific and Engineering Applications. Kluwer Academic Publishers (1998)
Hopcroft, J., Ullman, J.: Introduction to Automata Theory, Languages and Computation. Addison Wesley (1979).
Özden, B., Ramaswamy, S., Silberschatz, A.: Cyclic association rules. In Proceedings of the International Conference on Data Engineering (1998) 412–421
Ng, R., Lakshmanan, L., Han, J.: Exploratory Mining and Pruning Optimizations of Constrained Association Rules. In Proceedings of the International Conference on Management of Data (1998) 13–24
Ramaswamy, S., Mahajan, S., Silberschatz, A.: On the Discovery of Interesting Patterns in Association Rules. In Proceedings of the International Conference on Very Large Databases (1998) 368–379
Searls, D.B.: The Linguistics of DNA. American Scientist, 80 (1992) 579–591
Shahar, Y., Musen, M.A.: Knowledge-Based Temporal Abstraction in Clinical Domains. Artificial Intelligence in Medicine 8, (1996) 267–298
Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In Proceedings of the International Conference on Extending Database Technology (1996) 3–17
Zaki, M., Toivonen, H., Wang, J.: Report on BIOKDD01: Workshop on Data Mining in Bioinformatics. In SIGKDD Explorations, Volume 3, Issue 2 (2002) 71–73
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Antunes, C.M., Oliveira, A.L. (2002). Inference of Sequential Association Rules Guided by Context-Free Grammars. In: Adriaans, P., Fernau, H., van Zaanen, M. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2002. Lecture Notes in Computer Science(), vol 2484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45790-9_1
Download citation
DOI: https://doi.org/10.1007/3-540-45790-9_1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44239-4
Online ISBN: 978-3-540-45790-9
eBook Packages: Springer Book Archive