Multi-objective genetic algorithm based clustering approach and its application to gene expression data

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

Volumn 3261, Issue , 2004, Pages 451-461

  Özyer, Tansel ^a   Liu, Yimin ^a   Alhajj, Reda ^a   Barker, Ken ^a  

^a UNIVERSITY OF CALGARY   (Canada)

Author keywords

Clustering; Gene expression data analysis; Multi objective genetic algorithm; Validity analysis

Indexed keywords

CLUSTER ANALYSIS; GENE EXPRESSION; GENETIC ALGORITHMS; PARETO PRINCIPLE; BIOINFORMATICS; GENES; INFORMATION SYSTEMS;

BASED CLUSTERING; CLUSTERING APPROACH; CLUSTERINGS; GENE EXPRESSION DATA; GENE EXPRESSION DATA ANALYSIS; ITS APPLICATIONS; MULTI-OBJECTIVES GENETIC ALGORITHMS; NUMBER OF CLUSTERS; OPTIMAL NUMBER; VALIDITY ANALYSIS; CLUSTERING; CLUSTERING RESULTS; MULTI-OBJECTIVE GENETIC ALGORITHM; PARETO-OPTIMAL FRONT;

CLUSTERING ALGORITHMS;

EID: 35048821949     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30198-1_46     Document Type: Article

References (27)

1. Y. Barash and N. Friedman. Context-specific Bayesian clustering for gene expression data. Proc. of RECOMB, pp. 12-21,2001.
2. Ben-Dor, R. Shamir, and Z. Yakhini, Clustering gene expression patterns, Journal of Computatonal Biology, 1999.
3. D.L. Davies and D.W. Bouldin, A cluster separation measure, IEEE Transactions on Pattern Recognition and Machine Intelligence, No.l, pp.224-227, 1979.
4. K. Deb, et al., "A Fast Elitist Non-Dominated Sorting Genetic Algorithm for MultiObjective Optimization: NSGA-II," Proc. of the Parallel Problem Solving from Nature. Springer LNCS No. 1917, Paris, France, 2000.
5. J. Dunn, Well separated clusters and optimal fuzzy partitions, Journal of Cybernetics, Vol.4, pp.95-104, 1974.
6. J. Grabmeier, et al, Techniques of Cluster Algorithms in Data Mining, Kluwer Academic Publishers, Data Mining and Knowledge Discovery, Vol.6, pp.303-360, 2003.
7. Gene Expression Data of the Genomic Resources, University of Stanford (Available at: http://genome-www.stanford.edu/serum/data.html, downloaded in May 2004).
8. T.R. Golub, et al, Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science, 286, pp.531-537, 1999.
9. M. Halkidi, M. Vazirgiannis and I. Batistakis, Quality scheme assessment in the clustering process, Proceedings of PKDD, Lyon, France, 2000.
10. M. Halkidi, M. Vazirgiannis, Clustering Validity Assessment: Finding the optimal partitioning of a data set, Proceedings of lEEE ICDM, California, Nov. 2001.
11. J.A. Hartigan, Clustering Algorithms, New York: John Wiley and Sons, 1975.
12. J. Horn, N. Nafpliotis, and D. E. Goldberg, A niched pareto genetic algorithm for multiobjective optimization, Proceedings of IEEE CEC, IEEE World Congress on Computational Computation, Vol.1, pp.82-87, Piscataway, NJ., 1994.
13. L. Hubert and J. Schultz, Quadratic assignment as a general data-analysis strategy. British Journal of Mathematical and Statistical Psychologies, Vol.29, pp. 190-241, 1976.
14. V.R. lyer, et al, The transcriptional program in the response of human fibroblasts to serum, Science 283(5398), pp.83-7, 1999.
15. K. Jain, et al, Data Clustering: A Review, ACM Sun-eys, Vol.31, No.3, 1999.
16. T. Kohonen, Self-organizing Maps, Berlin/Heidelberg: Springer-Verlag, 1997.
17. Y. Liu, T. Özyer, R. Alhajj, and K. Barker, Validity Analysis of Clustering Obtained Using Multi-Objective Genetic Algorithm, Proc. of lEEE ISDA, 2004.
18. Y. Lu, et al, FGKA: A Fast Genetic K-means Clustering Algorithm, Proc. of ACM Symposium on Applied Computing, pp.162-163, Nicosia, Cyprus, 2004.
19. J. MacQueen, Some methods for classification and analysis of multivariate observations, Proc. of Berkeley Symposium on Math Stat Probability (Edited by: University of California Press), Cam LML, Neyman J, pp.281-297, 1965.
20. B.J.T. Morgan and A.P.G Ray, Non-uniqueness and inversions in cluster analysis, Applied Statisics, Vol.44, pp.114-134.
21. P.J. Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, Journal of Comp App. Math, Vol.20, pp.53-65, 1987.
22. U. Scherf, et al, A Gene Expression Database for the Molecular Pharmacology of Cancer, Nat Genet 24, pp.236-44, 2000.
23. R. Shamir and R. Sharan, Algorithmic approaches to clustering gene expression data: Current Topics in Computational Biology, MIT Press, 2001
24. P. Tamayo, et al, Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation, Proc. of. Nat'l Acad Sci USA, 96,pp.2907-2912, 1999.
25. K. Tamura, et al, Necessary and Sufficient Conditions for Local and Global NonDominated Solutions in Decision Problems with Multi-objectives, Journal of Optimization Theory and Applications, Vol.27, pp.509-523, 1979.
26. S. Theodoridis and K. Koutroumbas, Pattern Recognition, Academic Press, 1998.
27. K.Y. Yeung, et al, Model-based clustering and data transformations for gene expression data. Bioinformatics, Vol.17, pp.977-987,2001.