基于MapReduce的K-Medoids并行算法

为了解决传统K-Medoids聚类算法在处理海量数据信息时所面临的内存容量和CPU处理速度的瓶颈问题，在深入研究K-Medoids算法的基础之上，提出了基于MapReduce编程模型的K-Medoids并行化算法思想。Map函数部分的主要任务是计算每个数据对象到簇类中心点的距离并（重新）分配其所属的聚类簇；Reduce函数部分的主要任务是根据Map部分得到的中间结果，计算出新簇类的中心点，然后作为中心点集给下一次MapReduce过程使用。实验结果表明：运行在Hadoop集群上的基于MapReduce的K-Medoids并行化算法具有较好的聚类结果和可扩展性，对于较大的数据集，该算法得到的加速比更接近于线性。

Parallel K-Medoids algorithm based on MapReduce

In order to solve the bottleneck problems of memory capacity and CPU processing speed when the traditional K-Medoids clustering algorithm is used to deal with massive data, based on the in-depth study of K-Medoids algorithm, a parallel K-Medoids algorithm based on the MapReduce programming model was proposed. The part of Map function is to calculate the distance of each data object to the center point of the cluster and (re)allocation of their respective clusters, and the part of Reduce function is to calculate the new center point of each cluster according to the intermediate results of the Map section. The experimental results show that the parallel K-Medoids algorithm in the Hadoop cluster based on the MapReduce running has good clustering results and scalability, and for large data sets, the algorithm may get close to linear speedup.