基于向量矩阵的Apriori改进算法研究  

An improved Apriori algorithm based on vector matrix

作　　者：裘慧奇 QIU Huiqi(Office of Information,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学信息化办公室,上海200093

出　　处：《上海理工大学学报》2022年第1期56-61,68,共7页Journal of University of Shanghai For Science and Technology

基　　金：国家自然科学基金资助项目(61472256,61170277)。

摘　　要：针对传统的关联分析算法Apriori执行效率低、I/O过重、计算量过大等问题,提出了一种通过减少扫描数据库次数来降低候选项集计算复杂度,在频繁项集求解过程中通过将事务项集转换为行向量,利用“与”操作来提高算法执行效率的Apriori改进算法。利用学生在校行为数据集对Apriori改进算法进行有效性和高效性验证。同时,为了符合算法对样本数据的要求,在样本数据处理过程中对原始数据进行了清洗和离散化处理,定义了分析对象的样本数据离散化处理的规则。通过实验分析比较了Apriori改进算法与经典Apriori算法的性能。结果表明,Apriori改进算法保持了对实际分析对象关联规则挖掘的有效性,同时具有更高的执行效率。Aiming at the problems of low execution efficiency,excessive I/O burden and large amount of calculation of traditional association analysis Apriori algorithm,an improved Apriori algorithm was proposed,which reduced the computational complexity of the candidate item set by reducing the number of database scans,and improved the execution efficiency of the algorithm by converting the transaction item set into a row vector and using the“and”operation.The effectiveness and efficiency of the improved Apriori algorithm were verified by using the data set of students’behavior.At the same time,in order to meet the requirements of the algorithm for sample data,the original data were cleaned and discretized in the process of sample data processing,and the rules for discretization of sample data of the analysis object were defined.The performance of improved Apriori algorithm and classical Apriori algorithm was analyzed and compared through experiments.The results show that the improved Apriori algorithm maintains the effectiveness of mining association rules for actual analysis objects,and has higher execution efficiency.

关 键 词：数据挖掘 关联分析 向量矩阵 APRIORI改进算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]