基于SOA的改进型Apriori算法

Improved Apriori algorithm based on SOA

针对传统的粗糙集挖掘方法易受到频繁项集中干扰项的影响,导致挖掘精度低的问题,提出基于面向服务构架和时频特征提取的频繁项集关联规则挖掘Apriori算法.通过频繁模式树下的面向服务构架SOA模型,建立频繁项挖掘的频繁模式树,并进行信息融合预处理,构建频繁项集关联规则概念格,并提取数据时频特征,实现Apriori算法改进.仿真结果表明,采用改进Apriori算法进行关联规则的频繁项集挖掘时,数据特征提取精度较高,所需时间少,挖掘精度及指标性能均优于传统方法.

In the view of the traditional method of rough set mining method easily affected by frequent items focused distractions, leading to the problem of low digging precision, based on service oriented architecture and time-frequency feature extraction, Apriori algorithm for mining association rules is proposed. Through frequent pattern tree of service-oriented architecture SOA model, the FP-tree of frequent items mining is set up, information fusion is preprocessed, concept lattice and association rule are built and data frequent item sets time-frequency feature is extracted, achieving improved Apriori algorithm. The simulation results show that with the improved algorithm the data feature extraction has high precision, less time required~ mining precision and performance indicators are better than those of traditional methods.