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Performance evaluation of cluster algorithms for Big Data analysis on cloud

机译:云大数据分析集群算法的性能评估

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In this study, based on clustering algorithms, we perform data mining on land price data in Taichung City during past ten years. For big data analysis, we combine Hadoop HDFS and MapReduce with R language and visualize results on Google Maps. We also study performances of K-means and Fuzzy C-means clustering algorithms, executed in the Hadoop cloud and a stand-alone PC. The experimental results show that with a cloud of 9 compute nodes, about 3.5 times of acceleration are attainable; hence Hadoop cloud with R can be applied to solving insufficient memory issues in big data applications.
机译:在本研究中,基于聚类算法,我们在过去十年中对台中市的土地价格数据进行了数据挖掘。对于大数据分析,我们将Hadoop HDFS和MapReduce与R语言组合并在Google地图上可视化结果。我们还研究K-Milit和模糊C-Means聚类算法的表演,在Hadoop云和独立PC中执行。实验结果表明,通过9个计算节点的云,可以获得约3.5倍的加速度;因此,Hadoop云与R可以应用于在大数据应用中解决内部内存问题。

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