【摘要】 对于具有海量信息的个性化推荐问题,K-means聚类算法的传统实现方式已不能快速准确地满足要求。基于目前最为流行的开源云计算平台Hadoop及分布式计算框架Map Reduce,实现K-means聚类算法的并行化。给出该算法的具体实现,实验表明能够较好地解决时间瓶颈问题。更多还原

【Abstract】 In terms of the personalized recommendation problem of mass information, the traditional implementation of K-means clustering algorithm can not meet the requirements. Based on the most popular open source cloud computing platform Hadoop and distributed computing framework, realizes the parallelization of K-means clustering algorithm. Finally gives a concrete realization of this algorithm and the ex-periment shows that the problem of time bottleneck can be well solved.更多还原