A simulated annealing approach to find the optimal parameters for fuzzy clustering microarray data

Proceedings - International Conference of the Chilean Computer Science Society, SCCC

Volumn 2005, Issue , 2005, Pages 45-54

  Yang, Wei ^a   Rueda, Luis ^a   Ngom, Alioune ^a  

^a UNIVERSITY OF WINDSOR   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; FUZZY CLUSTERING; GENE EXPRESSION; PARAMETER ESTIMATION; SELF ORGANIZING MAPS; SIMULATED ANNEALING;

EXPRESSION ANALYSIS; HIERARCHICAL CLUSTERING; OPTIMAL PARAMETERS;

MICROARRAYS;

EID: 33947264136     PISSN: 15224902     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SCCC.2005.1587865     Document Type: Conference Paper

References (18)

1. J. Bezdek. Pattern Recognition with Fuzzy Objective Functin Algorithms. Kluwer Academic Publishers, Norwell, MA, USA, 1981.
2. R. Cho, M. Campbell, E. Winzeler, L. Steinmetz, A. Conway, L. Wodicka, T. Wolfsberg, A. Gabrielian, D. Landsman, D. Lockhart, and R. Davis. A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell, 12:65-73, 1998.
3. D. Dembele and P. Kaster. Fyzzy c-means method for clustering microarray data. Bioinformatics, 19(8):973-980, 2003.
4. S. Draghici. Data Analysis Tools for DNA Microarray. Chapman & hall CRC, 2003.
5. M. S. Elmohamed, P. Coddington, and G. Fox. A comparison of annealing techniques for academic course scheduling. In Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II, pages 92-114. Springer-Verlag, 1998.
6. M. Futschik and N. Kasabov. Fuzzy clustering of gene expression data. Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference, 1:414 -419, 2002.
7. A. Gasch and M. Eisen. Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biology, 2002.
8. K. Hoffmann and P. Salamon. The optimal simulated annealing schedule for a simple model. J. Phys. A: Math. Gen, 23:3511-3523, 1989.
9. S. Kirkpatrick, C. Gelatt, and M. Vecchi. Optimization by simulated annealing. Science, 220:671-680, 1983.
10. T. Kohonen. The self-organizing map. Proc IEEE, 78:1464-1480, 1990.
11. D. Lockhart and E. Winzeler. Genomics, gene expression and dna arrays. Nature, 405:827-846, 2000.
12. A. Lukashin and R. Fuchs. Analysis of temporal gene expression profiles: Clustering by simulated annealing and determining the optimal number of clusters. Bioinformatics, 17(5):405-414, 2000.
13. U. Maulik and S. Bandyopadhyay. Performance evaluation of some clustering algorithms and validity indices. IEEE Trans, Pattern analysis and machine intelligence, 24, 2002.
14. U. Maulik and S. Bandyopadhyay. Fuzzy partitioning using a real-coded variable length genetic algorithm for pixel classification. IEEE Trans. On Geosciences and Remote Sensing, 4(5), 2003.
15. S. Ray and R. Turi. Determination of number of clusters in k-means clustering and application in colour image segmentation. In 4th International Conference on Advances in Pattern Recognition and Digital Techniques (ICAPRDT'99). 1999.
16. X. Wang, G. Whitwell, and J. Garibaldi. The application of a simulated annealing fuzzy clustering algorithm for cancer diagnosis. In 4th International Conference on Intelligent Systems Design and Application, 2004.
17. W. Yang. Optimizing parameters in fuzzy k-means for clustering microarray data. Master's thesis, University of Windsor, 2005. In Preparation. Available at http://davinci.newcs.uwindsor.carangom/papers/WeiThesis.pdf.
18. R. Young. Biomedical discovery with dna arrays. Cell, 102:9-15, 2000.