Efficient discovery of proximity patterns with suffix arrays

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

Volumn 2089, Issue , 2001, Pages 152-156

  Arimura, Hiroki ^a,b   Asaka, Hiroki ^a   Sakamoto, Hiroshi ^a   Arikawa, Setsuo ^a  

^a KYUSHU UNIVERSITY   (Japan)

^b JAPAN SCIENCE AND TECHNOLOGY AGENCY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

FORESTRY; PATTERN MATCHING; TEXT MINING;

EFFICIENT IMPLEMENTATION; INDEX STRUCTURE; PATTERN DISCOVERY; SUFFIX ARRAYS; SUFFIX-TREES;

TREES (MATHEMATICS);

EID: 84957030244     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-48194-x_14     Document Type: Conference Paper

References (15)

1. H. Arimura, J. Abe, R. Fujino, H. Sakamoto, S. Shimozono, S. Arikawa, Text Data Mining: Discovery of Important Keywords in the Cyberspace, In Proc. IEEE Kyoto Int'l Conf. Digital Library, 2001. (to appear)
2. H. Arimura, S. Arikawa, S. Shimozono, Efficient discovery of optimal word association patterns in large text databases, New Generation Computing, Special issue on Discovery Science, 18, 49-60, 2000.
3. Arimura, H., Wataki, A., Fujino, R., Arikawa, S., A fast algorithm for discovering optimal string patterns in large text databases, In Proc. The 9th Int. Workshop on Algorithmic Learning Theory (ALT'98), LNAI 1501, 247-261, 1998.
4. L. Devroye, L. Gyorfi, G. Lugosi, A Probablistic Theory of Pattern Recognition, Springer-Verlag,1996.
5. G. Gonnet, R. Baeza-Yates and T. Snider, New indices for text: Pat trees and pat arrays, In William Frakes and Ricardo Baeza-Yates (eds.), Information Retrieval: Data Structures and Algorithms, 66-82, 1992.
6. D. Gusfield, Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, Cambridge University Press, New York, 1997.
7. T. Kasai, G. Lee, H. Arimura, S. Arikawa, K. Park, Linear-time longest-common prefix computation in suffix arrays and its applications, In Proc. CPM'01, LNCS, Springer-Verlag, 2000 (this volumn). (A part of this work is also available as: T. Kasai, H. Arimura, S. Arikawa, Efficient substring traversal with suffix arrays, DOI-TR 185, 2001, ftp://ftp.i.kyushu-u.ac.jp/pub/tr/trcs185.ps.gz.)
8. M.J. Kearns, R.E. Shapire, L.M. Sellie, Toward efficient agnostic learning. Machine Learning, 17(2-3), 115-141, 1994.
9. D. Lewis, Reuters-21578 text categorization test collection, Distribution 1.0, ATandT Labs-Research, http://www.research.att.com/~lewis/, 1997.
10. E.M. Mc Creight, A space-economical suffix tree construction algorithm, JACM, 23(2):262-272, 1976.
11. U. Manber and R. Baeza-Yates, An algorithm for string matching with a sequence of don't cares. IPL 37, 1991.
12. U. Manber and G. Myers, Suffix arrays: A new method for on-line string searches, SIAM J. Computing, 22(5), 935-948 (1993).
13. S. Morishita, On classification and regression, In Proc. Discovery Science '98, LNAI 1532, 49-59, 1998.
14. B. Schieber and U. Vishkin, On finding lowest common ancestors: simplifications an parallelization, SIAM J. Computing, 17, 1253-1262, 1988.
15. J.T.L. Wang, G.W. Chirn, T.G. Marr, B. Shapiro, D. Shasha and K. Zhang, In Proc. SIGMOD'94, 115-125, 1994.