Abstract
Frequent string mining is the problem of finding frequently appearing strings in a given string database or large text. It has many applications to string data analysis on strings such as texts, word sequences, and genome sequences. The problem becomes difficult if the string patterns are allowed to match approximately, i.e., a fixed number of errors leads to an explosion in the number of small solutions, and a fixed ratio of errors violates the monotonicity that disables hill climbing algorithms, and thus makes searching difficult. There would be also a difficulty on the modeling of the problem; simple mathematical definitions would result explosion of the solutions. To solve this difficulty, we go back to the motivations to find frequent strings, and propose a new method for generating string patterns that appear in the input string many times. In the method, we first compute the similarity between the strings in the database, and enumerate clusters generated by similarity. We then compute representative strings for each cluster, and the representatives are our frequent strings. Further, by taking majority votes, we extend the obtained representatives to obtain long frequent strings. The computational experiments we performed show the efficiency of both our model and algorithm; we were able to find many string patterns appearing many times in the data, and that were long but not particularly numerous. The computation time of our method is practically short, such as 20 minutes even for a genomic sequence of 100 millions of letters.
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
Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. Journal on Molecular Biology 215, 403–410 (1990)
Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research 25, 3389–3402 (1997)
Hébert, C., Crémilleux, B.: Mining Frequent δ-Free Patterns in Large Databases. In: Hoffmann, A., Motoda, H., Scheffer, T. (eds.) DS 2005. LNCS (LNAI), vol. 3735, pp. 124–136. Springer, Heidelberg (2005)
Goethals, B.: The FIMI repository (2003), http://fimi.ua.ac.be/
Hou, M., Berman, P., Hsu, C.H., Harriset, R.S.: HomologMiner: Looking for Homologous Genomic Groups in Whole Genomes. Bioinformatics 23, 917–925 (2007)
Inokuchi, A., Washio, T., Motoda, H.: An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)
Manber, U., Myers, G.: Suffix Arrays: A New Method for On-line String Searches. SIAM J. on Comp. 22, 935–948 (1993)
Mitasiunaite, I., Boulicaut, J.-F.: Introducing Softness into Inductive Queries on String Databases. In: Databases and Information Systems IV, pp. 117–132. IOS Press (2007)
Pearson, W.R.: Flexible sequence similarity searching with the FASTA3 program package. Methods in Molecular Biology 132, 185–219 (2000)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In: ICDE 2001, pp. 215–224 (2001)
Price, A.L., Jones, N.C., Pevzner, P.A.: De novo Identification of Repeat Families in Large Genomes. Bioinformatics 21(suppl. 1), 351–358 (2005)
Roth, F.P., Hughes, J.D., Estep, P.W., Church, G.M.: Finding DNA Regulatory Motifs within Unaligned Noncoding Sequences Clustered by Whole-genome mRNA Quantitation. Nature Biotechnology 16, 939–945 (1998)
Saha, S., Bridges, S., Magbanua, Z.V., Peterson, D.G.: Computational Approaches and Tools Used in Identification of Dispersed Repetitive DNA Sequences. Tropical Plant Biol. (2008), doi:10.1007/s12042-007-9007-5
Uno, T.: Multi-sorting Algorithm for Finding Pairs of Similar Short Substrings from Large-scale String Data. Knowledge and Information System 25, 229–251 (2010)
Wang, J., Han, J.: BIDE: Efficient Mining of Frequent Closed Sequences. In: ICDE 2004, pp. 79–90 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Matsui, T., Uno, T., Umemori, J., Koide, T. (2013). A New Approach to String Pattern Mining with Approximate Match. In: Fürnkranz, J., Hüllermeier, E., Higuchi, T. (eds) Discovery Science. DS 2013. Lecture Notes in Computer Science(), vol 8140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40897-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-40897-7_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40896-0
Online ISBN: 978-3-642-40897-7
eBook Packages: Computer ScienceComputer Science (R0)