Abstract
Data clustering has been considered as one of the most important techniques for unsupervised learning in diverse applications. Gene clustering is to find out groups of genes similarly expressed in large size of microarray data. Meanwhile, recent development of microarray technology generates a very large number of microarray data with low cost and handles more than 10,000 genes simultaneously in one chip. Thus, high performance computing of gene clustering has become increasingly important in microarray data analysis. In this paper, we propose a scalable parallel gene clustering method using the MapReudce programming model. The proposed method utilizes the k-means algorithm for identifying similar groups of genes. Experiment results show that the proposed method can offer good scalability with data size increases, and different numbers of nodes, and it can also provide effective clustering results against real microarray data.
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Acknowledgment
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2013R1A1A2006236).
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Islam, A.K.M.T., Lim, CG., Jeong, BS. (2014). Parallel Gene Clustering Using MapReduce. In: Chen, Y., et al. Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science(), vol 8597. Springer, Cham. https://doi.org/10.1007/978-3-319-11538-2_34
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DOI: https://doi.org/10.1007/978-3-319-11538-2_34
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