Using Hybrid Hierarchical K-means (HHK) clustering algorithm for protein sequence motif Super-Rule-Tree (SRT) structure construction

International Journal of Data Mining and Bioinformatics

Volumn 4, Issue 3, 2010, Pages 316-330

  Chen, Bernard ^a   He, Jieyue ^b,c   Pellicer, Stephen ^b   Pan, Yi ^b  

^a UNIVERSITY OF CENTRAL ARKANSAS   (United States)

^b GEORGIA STATE UNIVERSITY   (United States)

^c SOUTHEAST UNIVERSITY   (China)

Author keywords

Clustering algorithm; HHK; Hybrid Hierarchical K means; Protein sequence motif; SRT; Super Rule Tree

Indexed keywords

PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; AUTOMATED PATTERN RECOGNITION; CHEMISTRY; CLUSTER ANALYSIS; PROCEDURES; PROTEIN MOTIF; PROTEIN SECONDARY STRUCTURE; SEQUENCE ANALYSIS;

ALGORITHMS; AMINO ACID MOTIFS; AMINO ACID SEQUENCE; CLUSTER ANALYSIS; PATTERN RECOGNITION, AUTOMATED; PROTEIN STRUCTURE, SECONDARY; PROTEINS; SEQUENCE ANALYSIS, PROTEIN;

EID: 77953143033     PISSN: 17485673     EISSN: 17485681     Source Type: Journal    
DOI: 10.1504/IJDMB.2010.033523     Document Type: Article

References (21)

1. Bradley, P. S. and Fayyad, U. M. (1998) 'Refining initial points for K-means clustering', Proc. 15th International Conf. on Machine Learning, Madison, Wisconsin, USA p. 727.
2. Brown, D. E. and Huntley, C. L. (1990) 'A practical application of simulated annealing to clustering', Pattern Recognition, Vol. 25, No. 4, pp. 401-412.
3. Chen, B., Pellicer, S., Tai, P. C., Harrison, R. and Pan, Y. (2007a) 'Super granular SVM feature elimination (Super GSVM-FE) model for protein sequence motif information extraction',. IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, 2007, CIBCB'07, Honolulu, Hawaii, USA, pp. 317-322.
4. Chen, B., Pellicer, S., Tai, P. C., Harrison, R. and Pan, Y. (2007b) 'Super granular shrink-SVM feature elimination (Super GS-SVM-FE) model for protein sequence motif information extraction', Bioinformatics and Bioengineering, 2007. BIBE 2007. Proceedings of the 7th IEEE International Conference on, Boston, MS, USA, pp. 379-386.
5. Chen, B., Pellicer, S., Tai, P. C., Harrison, R. and Pan, Y. (2008) 'Efficient super granular SVM feature elimination (Super GSVM-FE) model for protein sequence motif information extraction', Int. J. Functional Informatics and Personalised Medicine, Vol. 1, pp. 8-25.
6. Chen, B., Tai, P. C. and Harrison, R. (2005) 'Novel hybrid hierarchical-K-means clustering method (HK-means) for microarray analysis', Computational Systems Bioinformatics Conference, 2005, Workshops and Poster Abstracts. IEEE, Stanford University, California, USA, pp. 105-108.
7. Chen, B., Tai, P. C., Harrison, R. and Pan, Y. (2006) 'FGK model: an efficient granular computing model for protein sequence motifs information discovery', IASTED Proc. International Conference on Computational and Systems Biology (CASB), Dallas, pp. 56-61.
8. Crooks, G. E., Hon, G., Chandonia, J. M. and Brenner, S. E. (2004) 'WebLogo: a sequence logo generator', Genome Research, Vol. 14, pp. 1188-1190.
9. He, J., Chen, B., Hu, H. J., Harrison, R., Tai, P. C., Dong, Y. and Pan, Y. (2005) 'Rule clustering and super-rule generation for transmembrane segments prediction', IEEE Computational Systems Bioinformatics Conference Workshops (CSBW'05), Stanford University, California, USA, pp. 224-227.
10. Henikoff, S. and Henikoff, J. G. (1992) 'Amino acid substitution matrices from protein blocks', Proceedings of the National Academy of Sciences of the United States of America, Vol. 89, pp. 10915-10919.
11. Hu, J., Ray, B. K. and Singh, M. (2007) 'Statistical methods for automated generation of service engagement staffing plans-References', IBM Journal of Research and Development, Vol. 51, pp. 281-293.
12. Jain, A. K. and Dubes, R. C. (1988) Algorithms for Clustering Data, Prentice-Hall, Inc., Upper Saddle River, NJ, USA.
13. Kabsch, W. and Sander, C. (1983) 'Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features', Biopolymers, Vol. 22, pp. 2577-2637.
14. Karypis, G., Han, E. and Kumar, V. (1999) Multilevel Refinement for Hierarchical Clustering, Defense Technical Information Center.
15. MacQueen, J. (1967) 'Some methods for classification and analysis of multivariate observations', Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, California, USA, Vol. 1, p. 14.
16. Ohler, U. and Niemann, H. (2001) 'Identification and analysis of eukaryotic promoters: recent computational approaches', Trends in Genetics, Vol. 17, pp. 56-60.
17. Pelleg, D. and Moore, A. (2000) 'X-means: extending K-means with efficient estimation of the number of clusters', Proceedings of the 17th International Conf. on Machine Learning, Stanford University, California, USA, pp. 727-734.
18. Sander, C. and Schneider, R. (1991) 'Database of similarity derived protein structures and the structure meaning of sequence alignment', Proteins: Struct. Funct. Genet., Vol. 9, No. 1, pp. 56-58.
19. Wang, G. and Dunbrack, R. L. (2003) 'PISCES: a protein sequence culling server', Bioinformatics, Vol. 19, No. 12, pp. 1589-1591.
20. Zhang, T., Ramakrishnan, R. and Livny, M. (1996) 'BIRCH: an efficient data clustering method for very large databases', Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Canada, pp. 103-114.
21. Zhong, W., Altun, G., Harrison, R., Tai, P. C. and Pan, Y. (2005) 'Improved K-means clustering algorithm for exploring local protein sequence motifs representing common structural property', NanoBioscience, IEEE Transactions on, Vol. 4, pp. 255-265.