基于业务路径和频度矩阵的关联规则挖掘算法

关联规则挖掘为分析机载网络关联故障及提高排故效率提供了重要方法。分析了经典Apriori算法的局限性,结合机载网络领域知识、矩阵运算和频繁项集性质,提出一种高效的关联规则挖掘算法。应用机载网络故障具有的基于业务路径的关联特征,提出分块挖掘策略,从而实现挖掘过程的噪声隔离。提出频度矩阵和特征向量,结合矩阵特点和频繁项集性质,设计5个扫描策略,从而减少了循环次数和对比运算。与Apriori算法 相比,新算法能有效提高频繁项集的搜索速率。

Algorithm for Mining Association Rules Based on Application Paths and Frequency Matrix

Association rule mining is an important method to analyze the associated faults of the airborne network and improve the efficiency of faults diagnosis process.This paper analyzed the limitations of the classical Apriori algorithm,and proposed an efficient association rule mining algorithm,which is based on the knowledge of the airborne network,matrix operation and frequent item sets.Due to the association characteristics of the airborne network faults based on the application paths,this paper proposed a mining strategy of block mining,so as to realize the noise isolation in mining process.With the conception of frequency matrix and feature vector,5 kinds of scanning strategies were proposed,thereby reducing the number of cycles and the comparison operation.Comparing with the classical Apriori algorithm,the new algorithm can effectively improve the search efficiency of frequent itemsets.