Mutual information based distance measures for classification and content recognition with applications to genetics

IEEE International Conference on Communications

Volumn 2, Issue , 2005, Pages 820-824

  Dawy, Zaher ^a,c   Hagenauer, Joachim ^a   Hanus, Pavol ^a   Mueller, Jakob C ^b  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b INSTITUTE OF EPIDEMIOLOGY   (Germany)

^c AMERICAN UNIVERSITY OF BEIRUT   (Lebanon)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION);

CONTENT RECOGNITION; GENETICS; MUTUAL INFORMATION;

GENETIC ENGINEERING;

EID: 24344438662     PISSN: 05361486     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. S. Grumbach and F. Tahi, "A new challenge for compression algorithms: Genetic sequences," Journal on Information Processing and Management, vol. 30, no. 6, pp. 875-886, 1994.
2. M. Li, X. Chen, X. Li, B. Ma, and P. Vitanyi, "The similarity metric," in Proc. of the 14th annual ACM-SIAM symposium on Discrete algorithms (SODA), (Baltimore, Maryland), pp. 863-872, 2003.
3. X. Chen, M. Li, B. Ma, and J. Tromp, "DNACompress: fast and effective DNA sequence compression," Bioinformatics, vol. 18, no. 12, pp. 1696-1698, 2002.
4. M. Hagedoorn, Pattern matching using similarity measures. PhD thesis, Universiteit Utrecht, Netherlands, Sept. 2000.
5. J. G. Cleary and I. H. Witten, "Data compression using adaptive coding and partial string matching," IEEE Transactions on Communications, vol. COM-32, pp. 396-402, April 1984.
6. NCBI, "National center for biotechnology information."
7. R. Cilibrasi, "Clustering by compression," 2003.