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Reseach Article

Performance Analysis of Artificial Fish Swarm based Clustering for Gene Expression Data

Published on September 2016 by M. Raja, H. Hannah Inbarani, M.thangarasu
National Conference on lnnovation in Computing and Communication Technology
Foundation of Computer Science USA
NCICCT2016 - Number 1
September 2016
Authors: M. Raja, H. Hannah Inbarani, M.thangarasu
e42a4124-e250-4b2f-9c5e-888d4d42f99e

M. Raja, H. Hannah Inbarani, M.thangarasu . Performance Analysis of Artificial Fish Swarm based Clustering for Gene Expression Data. National Conference on lnnovation in Computing and Communication Technology. NCICCT2016, 1 (September 2016), 10-15.

@article{
author = { M. Raja, H. Hannah Inbarani, M.thangarasu },
title = { Performance Analysis of Artificial Fish Swarm based Clustering for Gene Expression Data },
journal = { National Conference on lnnovation in Computing and Communication Technology },
issue_date = { September 2016 },
volume = { NCICCT2016 },
number = { 1 },
month = { September },
year = { 2016 },
issn = 0975-8887,
pages = { 10-15 },
numpages = 6,
url = { /proceedings/ncicct2016/number1/25863-2028/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on lnnovation in Computing and Communication Technology
%A M. Raja
%A H. Hannah Inbarani
%A M.thangarasu
%T Performance Analysis of Artificial Fish Swarm based Clustering for Gene Expression Data
%J National Conference on lnnovation in Computing and Communication Technology
%@ 0975-8887
%V NCICCT2016
%N 1
%P 10-15
%D 2016
%I International Journal of Computer Applications
Abstract

The K-Means algorithm is the widely used clustering technique. The performance ofthe K-Means algorithm depends highly on original cluster centers and converges to local minima. This paper proposes hybrid Artificial Fish Swarm Means (AFSK-Means) based clustering algorithm, by combining Particle Swarm Optimization with K-Means (PSOK) and Artificial Fish Swarm Algorithm based K-Means (AFSA). The basic idea is to search around the global solution by AFSK-Means and to increase the information exchange among genes. The effectiveness of the clustering algorithm depends on finding optimal clusters. The Clustering result shows the improved performance of hybrid clustering algorithm AFSK-Means in finding the best solution compared with the algorithms K-Means and PSOK-Means.

References
  1. Brown P, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nat Genet 21:33–37.
  2. Brazma A, Robinson A, Cameron G, Ashburner M (2000) Onestop shop for microarray data. Nature 403:699–700
  3. Asyali MH et al (2006) Gene expression profile classification: a review. Curr Bioinform 1:55–73
  4. Dopazo J (2006) Functional interpretation of microarray experiments. OMICS 10:3
  5. Kerr G, Ruskin HJ, Crane M, Doolan P (2008) Techniques for clustering gene expression data. Comput Biol Med 38:283–293
  6. Hartigan JA, Wong MA (1979) A K-Means clustering algorithm. Appl Stat 28:126–130
  7. Du Z et al (2008) PK-Means: a new algorithm for gene clustering. Comput Biol Chem 32(4):243–247
  8. Sun J et al (2012) Gene expression data analysis with the clustering method based on an improved quantum behaved particle swarm optimization. Eng Appl Artif I
  9. Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceedings of the IEEE international conference on evolutionary computation, IEEE Press, Piscataway, NJ, pp 69–73
  10. Lam YK, Tsang PWM, Leung CS (2011) Improved gene clustering based on particle swarm optimization, K-Means, and cluster matching. In: ICONIP 2011, part , LNCS, Springer, Heidelberg, vol. 7062, pp 654–661
  11. Alizadeh AA et al (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403:503–511
  12. Spellman PT et al (1998) Comprehensive identification of cell cycle-regulated genes of the yeast. Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 9:3273– 3297.
  13. Chu S et al (1998) The transcriptional program of sporulation in budding yeast. Science 282:699–705.
  14. Troyanskaya O et al (2001) Missing value estimation methods for DNA microarrays. Bioinformatics 17:520–525
  15. Lockhart, D. et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat. Biotechnol, 14:1675–1680, 1996.
  16. Schena,M. , D. Shalon, R. Davis, and P. Brown. Quantitative monitoring of gene expression patterns with a compolementatry DNA microarray. Science, 270:467–470, 1995.
  17. Tefferi, A. , Bolander, E. , Ansell, M. , Webern, D. and Spelsberg C. Primer on Medical Genomics Part III: Microarray Experiments and Data Analysis. Mayo Clin Proc. , 77:927– 940, 2002.
  18. D'haeseleer, P. , Wen, X. , Fuhrman, S. , Somogyi, R. Mining the Gene Expression Matrix: Inferring Gene Relationships fromLarge Scale Gene Expression Data. Information Processing in Cells and Tissues, pages 203–212, 1998.
  19. Tavazoie, S. , Hughes, D. , Campbell, M. J. , Cho, R. J. and Church, G. M. Systematic determination of genetic network architecture. Nature Genet, pages. Essen, Michael B. , Spellman, Paul T. , Brown, Patrick O. and Botstein, David . Cluster analysis and display of genome-wide expression patterns. Proc. Natl.
  20. Brahma, Alvis and Vilo, Jaak. Minireview: Gene expression data analysis. Federation of European Biochemical societies, 480:17–24, June 2000
  21. Alizadeh, A. A. , Eisen, M. B. , Davis, R. E. , Ma, C. , Lossos, I. S. , Rosenwald, A. , Boldrick, J. C. , Sabet, H. , Tran, T. , Yu, X. , Powell, J. I. , Yang, L. , Marti, G. E. , Moore, T. , Hudson, J. Jr, Lu, L. , Lewis, D. B. , Tibshirani, R. , Sherlock, G. , Chan, W. C. , Greiner, T. C. , Weisenburger, D. D. , Armitage, J. O. , Warnke, R. , Staudt, L. M. et al. Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature, Vol. 403:503–511, February 2000.
  22. Golub T. R. , Slonim D. K. , Tamayo P. , Huard C. , Gassenbeek M. , Mesirov J. P. , Coller H. , Loh M. L. , Downing J. R. , Caligiuri M. A. , Bloomfield D. D. , and Lander E. S. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, Vol. 286(15):531–537, October 1999.
  23. M Thangarasu, H Hannah Inbarani, "Analysis of K-Means with Multi View Point Similarity and Cosine Similarity Measures for Clustering the Document", International Journal of Applied Engineering Research, Vol. 10, pp. 6672-6675, 2015.
  24. M Thangarasu, R Manavalan, "Design and Development of Stemmer for Tamil Language: Cluster Analysis", International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, pp. 813-818
  25. M Thangarasu, R Manavalan, "Stemmers for Tamil Language: Performance Analysis", International Journal of Computer Science & Engineering Technology, Vol. 4, pp. 902-908
  26. M Thangarasu, R Manavalan, "Tree-Based Mining with Sentiment Analysis for Discovering Patterns of Human Interaction in Meetings Tamil Document", International Journal of Computational Intelligence and Informatics, Vol. 3, pp. 151-159
Index Terms

Computer Science
Information Sciences

Keywords

Hybrid Evolutionary Optimization Algorithm Data Clustering K-meansclustering Artificial Fish Swarm Algorithm Particle Swarm Optimization