摘要
为提高聚类算法效率,提出一种基于动态云平台的快速闭树聚类并行算法。针对云计算平台Hadoop中任务的随机分配策略,给出一个满足最小化消耗成本的任务分配算法CDA-GA,并基于该算法提出动态云平台模型。将传统的频繁闭树挖掘算法与聚类算法并行化,应用于动态云平台中,设计基于动态云平台的闭树聚类算法框架。实验结果表明,该算法有效可行,适合在大规模数据下进行聚类分析。
In order to improve the efficiency of clustering algorithm, this paper proposes a model of fast closed tree paralleled algorithm on the platform of dynamic cloud. Aiming at the random allocation strategy of cloud computing platform Hadoop, the paper puts forward CDA-GA to meet the requirements of the minimized consumption cost. Moreover, on the foundation of CDA-GA, it proposes the dynamic cloud platform model. The parallelization of traditional frequency closed tree mining algorithm and clustering algorithm and is applied in the dynamic cloud platform, this paper designs a closed tree clustering algorithm framework. Experimental results show that the algorithm is feasible and fits into clustering analysis under massive amounts of data.
出处
《计算机工程》
CAS
CSCD
2013年第9期80-83,共4页
Computer Engineering
基金
湖南省教育厅基金资助一般项目(10C1100)
吉首大学校级科研计划基金资助项目(11JD051)
关键词
数据挖掘
云计算
并行计算
闭树
树聚类
海量数据
data mining
cloud computing
parallel computing
closed tree
tree clustering
mass data
