基于压缩矩阵方式的Apriori改进算法

The Improved Apriori Algorithm Based on Compression Matrix Approach

针对关联规则中Apriori算法的不足之处,提出两种基于压缩矩阵方式的Apriori改进算法,改进算法充分利用矩阵并对其进行压缩,以大幅度减少扫描数据库的次数,并提高频繁项集的生成效率,从而有效提升算法的运算效率,同时,.,通过实例应用和算法性能兮析证明所提出的两种改进算法部是有效的关联规则挖掘方法。且比Apri算法具有最好的性能.

For the inadequacy of Apriori algorithm in association rules, this paper presents two methods of Apriori algorithm based on compression matrix approach. Improved algorithms make full use of the matrix and give compression on it, to significantly reduce the number of scans the database, and improve the generation efficiency of the frequent itemsets, and then improve the efficiency of the algorithm effectively. Meanwhile, the application example and algorithm performance analysis shows that the proposed two improved algorithms are efficient association rule mining method, and the properties are better than the Apriori algorithm.