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Improvements and Implementation of Hierarchical Clustering based on Hadoop

机译:基于Hadoop的分层聚类的改进与实现

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As the traditional agglomerative hierarchical clustering has a higher number of iterations which makes low efficiency of parallel realization on Hadoop, we propose an improved hierarchical clustering method: when the between-class distance is monotonically increasing, by changing the clustering order of hierarchical clustering without changing the final clustering result, aggregate multiple classes in a MapReduce operation, to reduce the number of iterations then enhance the computational efficiency. The experiments show compared to traditional hierarchical clustering algorithm implemented in Hadoop, the improved algorithm implemented in Hadoop has greatly reduces the number of iterations and the computation time.
机译:随着传统的凝聚层次聚类具有更高数量的迭代,这在Hadoop上进行了低效率的平行实现,我们提出了一种改进的分层聚类方法:通过在不改变的情况下更改分层群集的聚类顺序时,当类距离逐步增加时 最终聚类结果,在MapReduce操作中聚合多个类,以减少迭代的数量,然后提高计算效率。 实验表明与Hadoop中实现的传统分层聚类算法相比,Hadoop中实现的改进算法大大减少了迭代的数量和计算时间。

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