基于矩阵压缩的加权关联规则挖掘算法

关联规则挖掘作为近年来的研究热点之一,其经典算法Apriori算法因需要多次扫描数据库且会产生大量候选项集,严重影响了关联规则的挖掘效率.在此基础上提出了一种基于矩阵压缩的加权关联规则挖掘算法,只需扫描一次数据库,并将其转换为0-1矩阵,根据相关性质对矩阵进行压缩,从而降低了算法执行过程中的计算量;同时,考虑到项目的重要性,采取加权的方法,用求概率的方式设置项目属性的权值.同Apriori算法相比,本算法在挖掘过程中能直接查找高阶频繁项集.实验结果表明,本算法能有效提高关联规则的挖掘效率.

A Weighted Association Rules Mining Algorithm Based on Matrix Compression

As one of the research hotspots in recent years,the association algorithm Apriori algorithm has a lot of candidate items because it needs to scan the database several times,which seriously affects the mining efficiency of association rules.On this basis,a weighted association rule mining algorithm based on matrix compression was proposed.The database only needs to be scanned once and converted into a 0-1 matrix,and the matrix is compressed according to the related properties,which reduces the amount of calculation of the algorithm execution.At the same time,taking into account the importance of the project,a weighted approach is adopted with the probability of the way to set the weight of the project attributes.Compared with Apriori algorithm,this algorithm can find high order frequent itemsets directly in the mining process.Experimental results show that this algorithm can effectively improve the mining efficiency of association rules.