Efficient string mining under constraints via the deferred frequency index

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5077 LNAI, Issue , 2008, Pages 374-388

  Weese, David ^a   Schulz, Marcel H ^b,c  

^a FREIE UNIVERSITÄT BERLIN   (Germany)

^b MAX PLANCK INSTITUTE FOR MOLECULAR GENETICS   (Germany)

^c INTERNATIONAL MAX PLANCK RESEARCH SCHOOL FOR COMPUTATIONAL BIOLOGY AND SCIENTIFIC COMPUTING   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; DATA MINING; DATA STRUCTURES; DECISION SUPPORT SYSTEMS; DNA SEQUENCES; ELECTRONIC COMMERCE; FILE ORGANIZATION; INFORMATION MANAGEMENT; KNOWLEDGE MANAGEMENT; MEDICAL APPLICATIONS; MINING; NUCLEIC ACIDS; ORGANIC ACIDS; SEARCH ENGINES;

E-COMMERCE; FREQUENCY INDICES; GENERAL APPROACH; NATURAL LANGUAGES; NOVEL ALGORITHMS; PROTEIN SEQUENCING;

MEDICAL COMPUTING;

EID: 48949093349     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-70720-2_29     Document Type: Conference Paper

References (26)

1. Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Min. Knowl. Discov. 15(1), 55-86 (2007)
2. Berry, M.J., Linoff, G.S.: Data Mining Techniques: For Marketing, Sales, and Customer Support, 1st edn., pp. 51-62. John Wiley & Sons, Chichester (1997)
3. Muthusamy, Y.K., Barnard, E., Cole, R.A.: Reviewing automatic language identification. IEEE Sig. Proc. Mag. 11(4), 33-41 (1994)
4. Zhang, M.Q.: Computational analyses of eukaryotic promoters. BMC Bioinformatics 8(Supp 6), S3 (2007)
5. Birzele, F., Kramer, S.: A new representation for protein secondary structure prediction based on frequent patterns. Bioinformatics 22(21), 2628-2634 (2006)
6. Redhead, E., Bailey, T.L.: Discriminative motif discovery in dna and protein sequences using the DEME algorithm. BMC Bioinformatics 8, 385 (2007)
7. Fischer, J., Heun, V., Kramer, S.: Fast frequent string mining using suffix arrays. In: IEEE ICDM 2005, pp. 609-612. IEEE Computer Society Press, Los Alamitos (2005)
8. Raedt, L.D., Jaeger, M., Lee, S.D., Mannila, H.: A theory of inductive query answering. In: IEEE ICDM 2002, pp. 123-130. IEEE Computer Society, Los Alamitos (2002)
9. Chan, S., Kao, B., Yip, C.L., Tang, M.: Mining emerging substrings. In: DASFAA 2003, pp. 119-126. IEEE Computer Society, Los Alamitos (2003)
10. Lee, S.D., Raedt, L.D.: An efficient algorithm for mining string databases under constraints. In: Goethals, B., Siebes, A. (eds.) KDID 2004. LNCS, vol. 3377, pp. 108-129. Springer, Heidelberg (2005)
11. Abouelhoda, M., Kurtz, S., Ohlebusch, E.: Replacing suffix trees with enhanced suffix arrays. Journal of Discrete Algorithms 2, 53-86 (2004)
12. Fischer, J., Heun, V., Kramer, S.: Optimal string mining under frequency constraints. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 139-150. Springer, Heidelberg (2006)
13. Fischer, J., Heun, V.: Theoretical and practical improvements on the RMQ-problem, with applications to LCA and LCE. In: Lewenstein, M., Valiente, G. (eds.) CPM 2006. LNCS, vol. 4009, pp. 36-48. Springer, Heidelberg (2006)
14. Manber, U., Myers, E.: Suffix arrays: a new method for on-line string searches. In: SODA 1990, pp. 319-327. SIAM, Philadelphia (1990)
15. Giegerich, R., Kurtz, S., Stoye, J.: Efficient implementation of lazy suffix trees. Software Pract. Exper. 33(11), 1035-1049 (2003)
16. Giegerich, R., Kurtz, S.: A comparison of imperative and purely functional suffix tree constructions. Sci. Comput. Program. 25, 187-218 (1995)
17. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: 8.2: Counting sort. In: Introduction to Algorithms, 2nd edn., pp. 168-170. MIT Press and McGraw-Hill (2001)
18. Fitzgerald, P.C., Sturgill, D., Shyakhtenko, A., Oliver, B., Vinson, C.: Comparative genomics of drosophila and human core promoters. Genome Biol. 7, R53 (2006)
19. The UniProt Consortium: The Universal Protein Resource (UniProt). Nucl. Acids Res. 36(suppl_1), D190-D195 (2008), ftp://ftp.ebi.ac.uk/pub/databases/ integr8/uniprot/proteomes
20. Asuncion, A., Newman, D.: UCI machine learning repository (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
21. Dong, G., Li, J.: Efficient mining of emerging patterns: discovering trends and differences. In: KDD 1999, pp. 43-52. ACM, New York (1999)
22. Kurtz, S.: Reducing the space requirement of suffix trees. Software Pract. Exper. 29(13), 1149-1171 (1999)
23. Li, J., Dong, G., Ramamohanarao, K.: Making use of the most expressive jumping emerging patterns for classification. In: PADKK 2000, pp. 220-232. Springer, Heidelberg (2000)
24. Mitasiunaite, I., Boulicaut, J.F.: Looking for monotonicity properties of a similarity constraint on sequences. In: SAC 2006, pp. 546-552. ACM, New York (2006)
25. Ji, X., Bailey, J., Dong, G.: Mining minimal distinguishing subsequence patterns with gap constraints. Knowl. Inf. Syst. 11(3), 259-286 (2007)
26. Döring, A., Weese, D., Rausch, T., Reinert, K.: SeqAn an efficient, generic C++ library for sequence analysis. BMC Bioinformatics 9, 11 (2008)