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.
出处
《计算机工程》
CAS
CSCD
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)
