Abstract
Machine learning is a burgeoning technology used for extractions of knowledge from an ocean of data. It has robust binding with optimization and artificial intelligence that delivers theory, methodologies and application domain to the field of statistics and computer science. Machine learning tasks are broadly classified into two groups namely supervised learning and unsupervised learning. The analysis of the unsupervised data requires thorough computational activities using different clustering algorithms. Microarray gene expression data are taken into consideration for cluster regulating genes from non-regulating genes. In our work optimization technique (Cat Swarm Optimization) is used to minimize the number of cluster by evaluating the Euclidean distance among the centroids. A comparative study is being carried out by clustering the regulating genes before optimization and after optimization. In our work Principal component analysis (PCA) is incorporated for dimensionality reduction of vast dataset to ensure qualitative cluster analysis.
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References
Ma, P.C.H., Chan, K.C.C., Xin, Y., Chiu, D.K.Y.: An evolutionary clustering algorithm for gene expression microarray data analysis. IEEE Trans. Evol. Comput. 10(3), 296–314 (2006)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining—Practical Machine Learning Tools and Techniques. Morgan Kaufmann (2005)
Thamaraiselvi, G., Kaliammal, A.: A data mining: concepts and techniques. SRELS J. Inform. Manage. 41(4), 339–348 (2004)
Roy, S., Chakraborty, U.: Introduction to soft computing: NeuroFuzzy and Genetic Algorithms. Pearson Publication
Dudoit, S., Gentleman, R.: Cluster analysis in DNA microarray experiments. Bioconductor Short Course Winter (2002)
Gibbons, F.D., Roth, F.P.: Judging the quality of gene expression-based clustering methods using gene annotation. Genome Res. 12(10), 1574–1581 (2002)
Deng, Y., Kayarat, D., Elasri, M.O., Brown, S.J.: Microarray data clustering using particle swarm optimization K-means algorithm. In: Proceedings 8th JCIS, pp. 1730–1734 (2005)
Lee, K.M., Chung, T.S., Kim, J.H.: Global optimization of clusters in gene expression data of DNA microarrays by deterministic annealing. Genom. Inform. 1(1), 20–24 (2003)
Dudoit, S., Gentleman, R.: Cluster analysis in DNA microarray experiments. Bioconductor Short Course Winter (2002)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Jiang, D., Chun, T., Aidong, Z.: Cluster analysis for gene expression data: A survey. IEEE Trans. Knowled. Data Eng. 16(11), 1370–1386 (2004)
Dey, L., Mukhopadhyay, A.: Microarray gene expression data clustering using PSO based K-means algorithm. UACEE Int. J. Comput. Sci. Appl. 1(1), 232–236 (2009)
Andreopoulos, B., An, A., Wang, X., Schroeder, M.: A roadmap of clustering algorithms: finding a match for a biomedical application. Briefings Bioinform. 10(3), 297–314 (2009)
Santosa, B., Ningrum, M.K.: Cat swarm optimization for clustering and pattern recognition. In: International Conference of Soft Computing SOCPAR’09, pp. 54–59. 20 (2009)
Yin, L., Huang, C.H., Ni, J.: Clustering of gene expression data: performance and similarity analysis. BMC Bioinform. (2006)
Priscilla, R., Swamynathan, S.: Efficient two dimensional clustering of microarray gene expression data by means of hybrid similarity measure. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics, pp. 1047–1053. ACM (2012)
Santosa, B., Ningrum, M.K.: Cat swarm optimization for clustering. In: International Conference of in Soft Computing and Pattern Recognition, pp. 54–59 (2009)
Iassargir, M., Ahhmad, A.: A hybrid multi-objective PSO method discover biclusters in microarray data. Mohsen. Int. J. Comput. (2009)
Karaboga, D., Ozturk, C.: A novel clustering approach: artificial bee colony (ABC) algorithm. Appl. Soft Comput. 652–657 (2011)
Castellanos-Garzón, J.A., Diaz, F.: An evolutionary and visual framework for clustering of DNA microarray data. J. Integr. Bioinform. 10, 232–232 (2012)
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Rana, M., Vijayeeta, P., Kar, U., Das, M., Mishra, B.S.P. (2016). Unsupervised Machine Learning Approach for Gene Expression Microarray Data Using Soft Computing Technique. In: Nagar, A., Mohapatra, D., Chaki, N. (eds) Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. Smart Innovation, Systems and Technologies, vol 43. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2538-6_51
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DOI: https://doi.org/10.1007/978-81-322-2538-6_51
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