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《Journal of Harbin University of Science and Technology》 2016-01
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Parallelization Study of Improved K-means Algorithm on MapReduce Programming Model

LI Lan-ying;DONG Yi-ming;KONG Yin;ZHOU Qiu-li;School of Computer Science and Technology,Harbin University of Science and Technology;  
Because the selection of the initial clustering center is not sure,K-means algorithm has slow convergence speed when it is dealing with massive amounts of data. This paper introduced an improved k-means algorithm. Firstly,the idea of fuzzy clustering is introduced to classify the datasets. Secondly,the datasets are reclassified by means of dynamic clustering center. Finally,the improved algorithm is tested on MapReduce programming model. The experimental results show that the improved algorithm not only has a higher speedup,but also has a faster convergence.
【Fund】: 黑龙江省教育厅科学技术研究项目(12531107)
【CateGory Index】: TP311.13
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