基于频繁2项集支持矩阵的Apriori改进算法  

Improved Apriori Algorithm Based on Frequency 2-item Set Support Matrix

作　　者：纪怀猛[1] 

机构地区：[1]福州大学阳光学院,福州350015

出　　处：《计算机工程》2013年第11期183-186,共4页Computer Engineering

基　　金：福建省教育厅基金资助项目(JB12255)

摘　　要：Apriori算法在关联规则挖掘过程中需要多次扫描事务数据库,产生大量候选项目集,导致计算量过大。为解决该问题,提出一种基于频繁2项集支持矩阵的Apriori改进算法,通过分析频繁k+1项集的生成机制,将支持矩阵与频繁2项集矩阵相结合实现快速剪枝,并大幅减少频繁k项集验证的计算量。实验结果表明,与Apriori算法和ABTM算法相比,改进算法明显提高了频繁项集的挖掘效率。As Apriori algorithm used for mining association rules can lead to a large number of candidate itemsets and huge computations, an improved Apriori algorithm based on frequency 2-item set support matrix is proposed. By analyzing the generation mechanism of frequent k＋l item sets, the improved algorithm combines assistant matrix and frequent 2-item matrix to realize rapid puming, it can trim infrequent item set quickly and reduce the amount of calculation of k frequent item set verification. Experimental result shows that frequent itemsets mining efficiency of improved algorithm increases significantly compared with Apriori algorithm and ABTM algorithm.

关 键 词：关联规则 布尔矩阵 APRIORI算法 频繁项集 支持矩阵 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]