Parallel K-PSO Based on MapReduce
K-means is widely used in scientific research and commercial applications because of its simplicity and linearity.However,in faced of ever-growing amount of data and higher demand of cluster analysis today,how to improve the performance of K-means has become challenging and significant.So an improved method called parallel K-PSO which combines Particle Swarm Optimization (PSO) with K-means based on MapReduce is proposed in this paper.Firstly,it takes advantage of PSO to improve the global search ability of K-means,and then it makes K-means parallel with MapReduce to enhance its capability of processing massive data.We evaluate the proposed method through experimental results.
PSO K-means Hadoop MapReduce
Junjun Wang Dongfeng Yuan Mingyan Jiang
School of Information Science and Engineering Shandong University, Jinan, China, 250100
国际会议
2012 IEEE 14th International Conference on Communication Technology(2012年第十四届通信技术国际会议(ICCT 2012))
成都
英文
1295-1300
2012-11-09(万方平台首次上网日期,不代表论文的发表时间)