M. Vilares
ABSTRACT
We present a proposal intended to demonstrate the applicability of tabulation techniques for detecting approximately common patterns when dealing with structures sharing some common parts. This sharing saves on the space needed to represent the structures and also on their later processing, by factorizing the filtering of substructure matching. As a consequence, preliminary experimental tests indicate a reduction of the running time.
- 1.S. Billot and 13. Lang. The structure of shared forest in ambiguous parsing. In Proc. 27th Annual Meeting of the ACL, 1989.]] Google Scholar
Digital Library
- 2.Mitchell. Machine learning and data mining. CACM: Communications of the ACM, 42, 1999.]] Google ScholarDigital Library
- 3.Kuo-Chung Tai. Syntactic error correction in programming languages. IEEE Transactions on Software Engineering, SE-4(5):414-425, 1978.]]Google ScholarDigital Library
- 4.M. Vilares and B.A. Dion. Efficient incremental parsing for context-free languages. In Proc. of the 5th IEEE International Conference on Computer Languages, pages 241-252, Toulouse, France, 1994.]]Google Scholar
- 5.M. Vilares, F.J. Ribadas, and V.M. Darriba. Approximate pattern matching in shared-forest. In Proc. of the 11th Int. Conj, and Workshop on Database and Expert Systems Applications (DEXA '00), pages 322-333, Greenwich, United Kingdom, 2000.]] Google ScholarDigital Library
- 6.M. Vilares, F.J. Ribadas, and V.M. Darriba. Approximate VLDC pattern matching in shared-forest. Lecture Notes in Artificial Intelligence, 2004:483-494, 2001.]] Google ScholarDigital Library
- 7.J.T.L. Wang, T.G. Marr, D. Shasha, B.A. Shapiro, and G.-W. Chirn. Discovering active motifs in sets of related protein sequences and using them for classification. Nucleic Acids Research, 22(14):2769-2775, August 1994.]]Google ScholarCross Ref
- 8.J.T.L. Wang, B.A. Shapiro, D. Shasha, K. Zhang, and C.Y. Chang. Automated discovery of active motifs in multiple RNA secondary structures. In Evangelos Simoudis, Jia Wei Han, and Usama Fayyad, editors, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), pages 70-75. AAAI Press, 1996.]]Google Scholar
- 9.J.T.L. Wang, B.A. Shapiro, D. Shasha, K. Zhang, and K.M. Currey. An algorithm for finding the largest approximately common substructures of two trees. IEEE nansactions on Pattern Analysis and Machine Intelligence, 20(8):889-895, 1998.]] Google ScholarDigital Library
- 10.K. Zhang, D. Shasha, and J.T.L. Wang. Approximate tree matching in the presence of variable length don't cares. Journal of Algorithms, 16( 1):33-66, January 1994.]] Google ScholarDigital Library
- 11.K. Zhang, J.T.L. Wang, and D. Shasha. On the editing distance between undirected acyclic graphs. In International Journal of Foundations of Computer Science, World Scientific, volume 7. 1996.]]Google ScholarCross Ref
Index Terms
- Approximately common patterns in shared-forests
Recommendations
Finding common structured patterns in linear graphs
A linear graph is a graph whose vertices are linearly ordered. This linear ordering allows pairs of disjoint edges to be either preceding (<), nesting (@__ __) or crossing (@__ __). Given a family of linear graphs, and a non-empty subset R@__ __{<,@__ __...
Common structured patterns in linear graphs: approximation and combinatorics
CPM'07: Proceedings of the 18th annual conference on Combinatorial Pattern MatchingA linear graph is a graph whose vertices are linearly ordered. This linear ordering allows pairs of disjoint edges to be either preceding (<), nesting (⊆) or crossing (≬). Given a family of linear graphs, and a non-empty subset R ⊆ {<,⊆, ≬}, we are ...
Comments