Abstract
The rule extraction phase plays a very important role in Context-Free Grammar induction systems. The mining of frequent patterns and rules is a costly task. The preprocessing of the training set provides a way to make the related methods more efficient. Apriori and FP-Growth algorithms are the standard methods for determination of frequent itemsets. Two novel methods are presented for pattern mining in this chapter. The first one is based on extended regular expressions with multiplicity approach. The second method is based on the theory of concept lattices.
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
References
Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)
D’Ulizia, A., Ferri, F., Grifoni, P.: A survey of grammatical inference methods for natural language learning. Artif. Intell. Rev. 36, 1–27 (2011)
Gusfield, D.: Algorithms on strings, trees and sequences: computer science and computational biology. Cambridge University Press, Cambridge (1997)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. ACM SIGMOD Rec. 29, 1–12 (2000)
Jaworski, M., Unold, O.: Improved tbl algorithm for learning context-free grammar. In: Proceedings of the International Multiconference on Computer Science and Information Technology, ISSN 1896–7094, pp. 267–274 (2007)
Nakamura, K., Matsumoto, M.: Incremental learning of context free grammars based on bottom-up parsing and search. Pattern Recogn. 38, 1384–1392 (2005)
Sakakibara, Y., Kondo, M.: Ga-based learning of context-free grammars using tabular representations. In: Machine Learning-International Workshop then Conference, pp. 354–360, Morgan Kaufmann Publishers (1999)
Sakakibara, Y.: Learning context-free grammars using tabular representations. Pattern Recogn. 38, 1372–1383 (2005)
Solan, Z., Horn, D., Ruppin, E., Edelman, S.: Unsupervised learning of natural languages. Proc. Nat. Acad. Sci. U.S.A. 102(33), 11629–11634 (2005)
Tóth, Zs., Kovács, L.: CFG extension for META framework. In: IEEE 16th International Conference on Intelligent Engineering Systems, pp. 495–500 (2012)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Rokach, L., Maimon, O. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer, Berlin (2010)
Unold, O., Jaworski, M.: Learning context-free grammar using improved tabular representation. Appl. Soft. Comput. (2009)
Acknowledgments
The described work was carried out as part of the TÁMOP-4.2.2/B-10/1-2010-0008 project in the framework of the New Hungarian Development Plan. The realization of this project is supported by the European Union, co-financed by the European Social Fund.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Tóth, Z., Kovács, L. (2014). Pattern Distillation in Grammar Induction Methods. In: Bognár, G., Tóth, T. (eds) Applied Information Science, Engineering and Technology. Topics in Intelligent Engineering and Informatics, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-01919-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-01919-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01918-5
Online ISBN: 978-3-319-01919-2
eBook Packages: EngineeringEngineering (R0)