基于关联规则挖掘的一种改进Apriori算法

关联规则挖掘是数据挖掘技术的一个重要分支,其中Apriori是目前最经典和具有影响力的关联规则挖掘算法.在分析研究关联规则挖掘中Apriori算法的基础上,针对Apriori算法中的两个主要操作--连接和剪枝进行改进,通过扫描1-项集、去除分解子集操作及不生成候选项集等多种策略结合的方法来减少连接操作的数据项数和算法运行过程中对数据库扫描的次数,最终使改进后的Apriori算法的性能得到提高.

An improved Apriori Algorithm based on mining association rule

Mining association rule has been regarded as one of the most important branches in Data Mining,in which Apriori algorithm is the most classical and influential method at present.After analyzing and studying the Apriori algorithm in Mining association rule deeply,the two primary steps in Apriori algorithm,the join step and the prune step,are improved in this paper.The performance of the Apriori algorithm has been improved by scanning frequent 1-itemsets and removing the operation of generating subsets and candidate itemsets,which are used to decrease the number of the joined itemsets and to reduce the database scanning frequency respectively.