云计算下的远距离无线混合传输数据弱关联挖掘算法

Weak Association Mining Algorithm for Long-distance Wireless Hybrid Transmission Data Under Cloud Computing

远距离无线混合传输数据规模庞大、结构复杂,为更高效地完成此类数据的处理与挖掘任务,提出了云计算下弱关联挖掘算法。以Hadoop分布式文件系统为基础,利用元数据节点、命名节点及数据节点等架构云计算平台。根据关联规则支持度、置信度与最小置信度、最小支持度的关系判定数据间弱关联性;基于Apriori算法的连接与剪枝操作构建弱关联挖掘算法。针对某通信企业的远距离无线混合传输数据,展开算法性能检测实验,实验结果表明,所提算法挖掘性与免疫性优势显著,能较好适应恶劣、复杂的传输条件,且具有优越的数据处理速度。

Long distance wireless hybrid transmission data have a large scale and complex structure.In order to complete the processing and mining of such data more efficiently,this paper proposes a weak association mining algorithm under cloud computing.Based on the Hadoop distributed file system,a cloud computing platform is constructed using metadata nodes,slave named nodes and data nodes.Weak correlations between data are determined based on the relationship between the support,confidence coefficient and minimum confidence coefficient and minimum support of association rules;a weak correlation mining algorithm is constructed based on the connection and pruning operations of the Apriori algorithm.For the long-distance wireless hybrid transmission data of a communication enterprise,an algorithm performance testing experiment is conducted.The experimental results show that the proposed algorithm has significant advantages in mining and immunity,can better adapt to harsh and complex transmission conditions,and has superior data processing speed.