基于关联规则的数据挖掘Apriori算法的两种优化分析

Analysis of Two Optimization Strategies for Apriori Algorithm of Data Mining Algorithm Based on Association Rules

关联规则的数据挖掘算法——Apriori算法,其优化的核心在于如何在找出事务数据库中所有的频繁数据项集方面.论文以关联规则挖掘算法中的经典Apriori算法为基础,从删除无效连接,以减少冗余的连接为前提条件,进而实现减少剪枝步中的判断量提出了Apriori—A算法;以候选事务数据库替代原数据库D,大幅减少扫描次数为前提,提出了Apriori—B算法.结合特定实例经对比分析和实验验证,改进后的算法在效率方面优于经典算法.

Data mining algorithm for association rules -Apriori algorithm optimization core lies in how to find all the frequent data sets in the transaction database. Based on the classic Apriori algorithm in association rule mining algorithm, this paper proposes Apriori-A algorithm from deleting invalid connections to reducing redundant connections as the premise condition, and then realizing the reduction of judgment quantity in pruning step;based on the premise that candidate transaction database replaces the original database D and significantly reduces the scanning times, Apriori-B algorithm is proposed. The improved algorithm is superior to the classical Apriori algorithm in terms of efficiency through comparative analysis and experimental verification with specific examples.