基于云计算的并行K-means聚类算法研究

Research on Parallel K-means Algorithm Based on Cloud Computing Platform

摘要:

目前数据呈爆炸式增长,海量存储状态,给聚类研究带来了诸如计算复杂性和计算能力不足都很多问题；而云计算平台通过负载均衡,动态配置大量的虚拟计算资源,有效地突破了耗时耗能的瓶颈,在海量数据挖掘中体现出了其独特的优势；文章深入研究了基于云计算平台Hadoop的并行K-means算法,并结合MapReduce分布式计算模型,给出了算法设计的方法和策略,包括MapReduce处理的map、shuffle和Reduce 3个过程,仿真结果表明K-means并行算法的效率较高。

Abstract:

Now with the explosive growth of data,there are a lot of problems such as complexity and lack of computing resources in cluster research . The cloud computing platform breaks the state of high energy-consuming and shows the advantages in the massive data mining through load balance and dynamic configuration of virtual computing resources. The parallel K-means algorithm based on Hadoop platform was designed .The algorithm methods and strategies were shown combined with MapReduce distributed computing model including map, shuffle and Reduce. Simulation results show that the parallel K-means algorithm has strong computing efficiency.