【作者】 韩涛；

【导师】 张春海；

【作者基本信息】 中国海洋大学 ， 计算机应用技术， 2005， 硕士

【摘要】 数据挖掘是从存放数据库、数据仓库或其它信息库中的大量数据中挖掘有趣知识的过程。它包含关联规则挖掘、预测、分类、聚类、演化分析等多种技术手段,其中关联规则挖掘是一种主要的也是用途最广的数据挖掘方法。 关联规则概念最早是由在IBM工作的Rakesh Agrawal博士于1993年提出的,用于刻划事务数据库中交易项目之间的关系,即频繁关系。其研究已有10余年时间并取得了很多成果,但还有很多问题亟待解决。本文对此作了详细介绍,并对关联规则挖掘理论特别是关联规则挖掘算法进行了研究,取得了一定的研究成果。 本文对关联规则挖掘的经典算法:Apriori、AprioriTid、AprioriHybrid算法、Apriori RFM算法、划分的算法和取样算法进行了研究,针对上述算法的不足提出了一种快速挖掘关联规则的算法AprioriTidHybrid。算法以经典的Apriori和AprioriTid为基础,针对其特点对算法提出了改进,在最初的阶段用Apriori算法,在后来的扫描中用AprioriTid;考虑了（?）可能比源数据库大的情况,在L₂的基础上产生,而不是在传统的C₂的基础上产生（?）,大大地压缩了（?）的规模;采用一种更为高效的DA_gen算法代替Apriori_gen来产生候选项集。实验结果表明,较Apriori和AprioriTid算法的性能有明显的提高。 本文提出的基于Apriori、AprioriTid的取样关联规则算法研究创新之处在于,提出了一种高效的关联规则挖掘算法FASTA:采用经典的FAST的算法思想选取样本,使得选取的样本更具有典型性和精确性;对所选取的样本用本文提出的AprioriTidHybrid进行关联规则的挖掘。在实验中同其它的算法进行了比较,改进的算法提高了挖掘性能。更多还原

【Abstract】 Data mining is the process of discovering interesting knowledge from large volumes of data which are stored either in databases, data warehouses, or other information repositories. It includes lots of technical measures such as association rule mining, prediction, classification, clustering and evolutionary analysis. Of these techniques, the association rule mining technique is the most important and also the most widely-used method.The concept of association rule was first proposed in 1993 by Dr. Rakesh Agrawal who was working at IBM, to describe the relationship between transactional items in transaction databases, i.e. the frequent relationship. Studies on this subject have been carried out for more than 10 years and have yielded many fruits, many problems are still existing which need urgent resolution. This paper gives a detailed introduction to the studies of this area and makes an in-depth exploration on the association rule mining theory, with the algorithms of association rule mining in particular, the study turns out some valuable results.The paper first studies some typical association rule mining algorithms such as Apriori, AprioriTid, AprioriHybrid, Apriori RFM, Partition Algorithms and Sampling Algorithms. Considering the defects of these typical algorithms, a new algorithm AprioriTidHybrid, which could extract association rule faster, is then introduced. AprioriTidHybrid, basing on the typical algorithms of Apriori and AprioriTid, uses Apriori at its initial phases, later changes to AprioriTid when searching; also gets （C₂|ˉ） from L₂ instead of C₂ by considering （C₂|ˉ） may be larger than the original databases, this improved algorithm significantly reduces the scales of （C₂|ˉ）; and gets candidate itemsets by efficient DAgen instead of Apriorigen. The experimental results show the new algorithm outperforms Apriori and AprioriTid.The innovation of the sampling association rule algorithm which is grounded on Apriori and AprioriTid is: （1） a new more efficient algorithm FASTA is proposed; （2） by adopting the typical FAST algorithm to choose samples, the chosen samples are more typical and accurate; （3）AprioriTidHybrid is employed to extract the chosen samples. The experimental results show that FASTA outperforms other algorithms in the performance.更多还原