关键词: K-Medoids, 云计算, MapReduce, 并行计算, Hadoop

Abstract: 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.

Key words: K-Medoids, cloud computing, MapReduce, parallel computing, Hadoop