【摘要】 针对关联规则数据挖掘的Apriori算法存在I/O负载大、计算速度慢和减枝过程中间结果多的问题,提出了一种基于矩阵加权的VMOApriori算法。该算法扫描一次数据库生成事务矩阵,并利用矩阵加权及其向量运算产生频繁项集,通过删减矩阵中事务及事务项压缩矩阵,减少了候选项集的冗余,加快了频繁项集的筛选速度。仿真表明,该算法降低了I/O负载,减少了中间结果数据量,提高了数据挖掘效率,验证了算法的有效性。更多还原

【Abstract】 In order to overcome the inadequacy of Apriori algorithm,an improved VMOApriori algorithm based on weighted matrix is proposed.This algorithm scans the database to generate the transaction matrix and operates with weighted matrix and vector to calculate the frequent itemsets,and compresses matrix to reduce the redundancy of the candidate sets,and accelerates screening speed of frequent itemsets.The simulation results show that the algorithm reduces the I/O load and the amount of the intermediate results,and improves the efficiency of data mining.更多还原