IJCTT - Comparative Study of Data Cluster Analysis for Microarray

Comparative Study of Data Cluster Analysis for Microarray <table style="width: 647px; height: 160px;"> <tbody style="background-color: #ffffff; text-align: left; border-width: 0px;"> <tr style="background-color: #ffffff; border-style: none; border-width: 0px;"> <td style="background-color: #ffffff; border-style: none; border-width: 0px;" rowspan="6">  <img src="/images/ijctt-book.png" alt="IJCOT-book-cover" />  </td> <td style="background-color: #ffffff; border-style: none; border-width: 0px;">International Journal of Computer Trends and Technology (IJCTT)</td> <td style="background-color: #ffffff; border-style: none; border-width: 0px;" rowspan="6">            <a href="http://www.ijcttjournal.org/Volume3/issue-3/IJCTT-V3I3P112.pdf" target="_blank"><img src="/images/PDF3.png" alt="" /></a></td> </tr> <tr style="border-width: 0px;"> <td style="background-color: #ffffff; text-align: left; border-width: 0px;">© - Issue 2012 by IJCTT Journal</td> </tr> <tr style="background-color: #ffffff; text-align: left; border-width: 0px;"> <td style="background-color: #ffffff; text-align: left; border-width: 0px;">Volume-3 Issue-3                            </td> </tr> <tr style="background-color: #ffffff; text-align: left; border-width: 0px;"> <td style="background-color: #ffffff; text-align: left; border-width: 0px;">Year of Publication : 2012</td> </tr> <tr style="background-color: #ffffff; text-align: left; border-width: 0px;"> <td style="background-color: #ffffff; text-align: left; border-width: 0px;">Authors :Lokesh Kumar Sharma, Sourabh Rungta </td> </tr> </tbody> </table> <img src="/images/citation.gif" alt="MLA" /> Lokesh Kumar Sharma, Sourabh Rungta "Comparative Study of Data Cluster Analysis for Microarray"International Journal of Computer Trends and Technology (IJCTT),V3(3):367-371 Issue 2012 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group. Abstract: -Microarray has been a popular method for representing biological data. Microarray technology allows biologists to monitor genome-wide patterns of gene expression in a high-throughput fashion. Clustering the biological sequences according to their components may reveal the biological functionality among the sequences. Data cluster analysis is an important task in microarray data. There is no clustering algorithm that can be universally used to solve all problems. Therefore in this paper comparative study of data cluster analysis for microarray is presented. Here the most popular cluster algorithms that can be applied for microarray data are discussed. The uncertainty of data, optimization and density estimation are considered for comparison. References- [1] A. L. Tarca, R. Romero, and S. Draghici, "Analysis of microarray experiments of gene expression profiling",American Journal of Obstetrics and Gynecology (2006) 195, pp. 373–88. [2] C. Escudero et al., "Classification of Gene Expression Profiles: Comparison of k-means and expectation maximization algorithms", IEEE Computer Society, 2008, pp. 831-836. [3] D. Dembele and P. Kastner, "Fuzzy C-means method for clustering microarray data", Bioinformatics, Vol. 19, Issue 8, 2003, pp. 973-980. [4] E. Naghieh and Y. Peng, “Microarray Gene Expression Data Mining: Clustering Analysis Review”, Techniques, 2009. [5] J. Sander, M. Ester, H. P. Kriegel and X. Xu, “Density- Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications”, Journal of Data Mining and Knowledge Discovery, Kluwer Academic Publishers vol. 2, 1998 pp. 169-194. [6] K. Krishna and M. Murty, “Genetic K-Means Algorithm”, IEEE Transactions on Systems Man. and Cybernetics vol. 29, NO. 3, 1999, pp. 433-439. [7] L. Kaufman and P. J. Rousseeuw, “Finding Group in Data: an Introduction to Cluster Analysis”, John Wiley and Sons, 1990. [8] L. Raczynski, J. Wozniak, T. Rubel and K. Zaremba,"Application of Density Based Clustering to Microarray Data Analysis", Int. Journal of Electronics and Telecommunications, 2010, Vol. 56, No. 3, pp. 281- 286. [9] M. Ester, H. P. Kriegel, J. Sander and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”, In: Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD’96), Portland, 2006, AAAI Press 291-316. [10] M. Zhang et al., "A fuzzy C-means algorithm using a correlation metrics and gene ontology", IEEE 19th Int. Conf. on Pattern Recognition, 2008. pp. 1-4. [11] P. Valarmathie, T. Ravichandran, K. Dinakaran, “Survey of Clustering Algorithms for Microarray Gene Expression Data”, European Journal of Scientific Research, Vol. 69, No. 1, 2012, pp. 5-20. [12] R. D. Bin and D. Risso,"Clustering via nonparametric density estimation:an application to microarray data", BMC Bioinformatics, 2011 pp. 102-105. [13] R. Suzuki and H. Shimodaira, “An application of multiscale bootstrap resampling to hierarchical clustering of microarray data: How accurate are these clusters?” proc. by the Fifteenth Int. Conference on Genome Informatics (GIW 2004). 2004. p. P034. [14] R. Xu and D. Wunsh, "Survey of Clustering Algorithms", IEEE Transactions on Neural Networks, Vol. 16, No. 3, May 2005, pp. 645- 678. [15] T. Kato, K. Fujimura, H. Tokutaka, "Analysis of DNA Microarray Data by Using Self-Organizing Maps", Genome Informatics 14, 2003, pp. 328-329. [16] Y. Lu, S. Lu, F. Fotouhi, Y. Deng, and S. Brown, “FGKA: A Fast Genetic K-means Clustering Algorithm”, ACM 1-58113-812-, 2004. [17] Y. Lu, S. Lu, F. Fotouhi, Y. Deng, and S. Brown, “Incremental genetic K-means algorithm and its application in gene expression data analysis”, BMC Bioinformatics 5:172, 2004. [18] K. Deb and A. R. Reddy, “Classification of Two and Multi Class Cancer Data Reliably using Multi Objective Evolutionary Algorithms”, IIT Kanpur, KanGAL Report Number 2003006. [19] http://www.iitk.ac.in/kangal/bioinfo.shtml valid on 21 May 2012. KeywordsMicroarray Data, Data Cluster Analysis, Bioinformatics.

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IJCTT - Comparative Study of Data Cluster Analysis for Microarray