改进的关联规则挖掘算法-MIFP-Apriori算法

Apriori算法是关联规则挖掘的经典算法,具有原理简洁、易编程实现等优点,得到广泛应用。针对该算法扫描数据库次数过多,产生大量冗余候选集的缺陷,在现有Apriori算法改进优化思想的基础上,结合矩阵、改进频繁模式树和计算候选集频数优化策略提出了一种改进的关联规则挖掘算法——MIFP-Apriori算法。实验表明,该算法能够将扫描数据库次数降低到一次,有效解决产生大量冗余候选集的缺陷,提高算法效率。

An improved association rule mining algorithm

As one of the research hotspots of data mining, association rule mining can mine the hidden relationship between data, and extract valuable association rules from it, so as to provide users with auxiliary decision-making functions. As one of the classic algorithms for mining association rules, Apriori algorithm has the advantages of simple principle and easy programming and is widely used. Apriori algorithm is used to scan the database too many times, resulting in a large number of redundant candidate set defects. Based on the research and reference to the existing Apriori algorithm to improve the optimization idea, an improved association rule mining algorithm is proposed with the combination of matrix, improved frequent pattern tree and computational candidate frequency optimization strategy.