关键词: [K]-Medoids, 分布式计算, Hadoop, 并行采样

Abstract:  In view of the traditional [K]-Medoids algorithm is sensitive to the initial clustering center, slow convergence speed, and in large data environment facing the bottleneck problem of memory and CPU processing speed, through improving the initial center options and replacement strategy of using the Hadoop distributed computing platform combined with parallel random sampling strategy based on Top [K], realizes a highly efficient and stable [K]-Medoids parallel algorithm, and by adjusting the Hadoop platform, realize the further optimization of the algorithm. Experiments show that the improved [K]-Medoids algorithm not only has a good speedup, the convergence and the clustering accuracy are also improved.

Key words: [K]-Medoids, distributed computation, Hadoop, parallel sampling