摘要
时态数据反映了事物发生发展的过程,对其进行数据挖掘是非常有意义的。而数据库中含有海量数据,挖掘出贴近现实意义的时态关联规则是很困难的。时态关联规则挖掘算法中对聚类思想的引用和对发现时态频繁项集的Apriori算法进行改进是关键的两步。本文讨论了Apriori算法的缺点和改进,提出了基于时态约束的关联规则挖掘的算法解决办法,并通过Visual Basic软件,以超市前端收款机所收集的信息为数据,对改进算法进行了测试验证,为算法的应用提供了依据。
Temporal data reflects the process of the occurrence and development of things. It is very meaningful for data digging out. While the database contains a lot of data, digging out the temporal association rules with realistic significance is very difficult. Reference to the clustering and improvement of the temporal frequent itemsets Apriori algorithm are two key steps of algorithm for digging out temporal association rules. This paper discusses the shortcomings of Apriori algorithm and its improvement. The solution of algorithm for digging out association rules based on temporal constraint is put forward. And through the Visual Basic software, the date collected by supermarket front cash register, test the improved algorithm, provide a basis for the application of the algorithm.
出处
《自动化技术与应用》
2015年第1期42-46,共5页
Techniques of Automation and Applications
基金
2014年度辽宁省社会科学规划基金项目(编号:L14BTJ002)
关键词
APRIORI算法
关联规则挖掘
时态约束
超市数据挖掘
Apriori algorithm
association rule digging out
temporal constraints
supermarket data digging out
