【作者】 赵纪涛；

【导师】 姜保庆；

【作者基本信息】 河南大学 ， 应用数学， 2008， 硕士

【摘要】 数据挖掘是目前信息科学领域最前沿的研究课题之一,在许多领域均有成功的应用范例。它可以发现一些隐藏在大量数据背后潜在的信息来预测事物发展趋势。关联规则挖掘是数据挖掘领域中一个重要研究方向。为解决量化关联规则挖掘过程中的“尖锐边界”问题,已有研究者将模糊集的有关概念引入到关联规则挖掘中,提出了“模糊关联规则”。模糊集理论能够实现数据的平滑过渡,使得挖掘出的关联规则具有高度的易理解性。但仍存在支持度定义不合理、主观地确定隶属函数等问题。本文就上述问题,给出了一些解决方案,主要工作如下:1、提出了一种新的支持度计算方法,并在此基础上,结合Apriori算法,提出了一种挖掘模糊关联规则算法。实验表明,该算法是有效的。2、模糊关联规则挖掘中需确定隶属函数,提出了一种基于FCM的自动确定样本隶属度函数的方法。该方法克服了人为确定隶属函数、带有主观性的缺陷。该方法在认知尚未成熟、缺乏先验知识的领域中具有应用前景。3、系统地分析了t-模算子对挖掘结果的影响,并通过实验研究t-模算子对挖掘算法性能的影响。更多还原

【Abstract】 Data mining currently is the research frontier within the information science field. It had success applications in many areas. It can find the potential knowledge which hides behind the large data to forecast the trend of things development.Association rule mining is one of the key points in the research field of data mining. To deal with the problem of sharp boundary in mining quantitative association rules, researchers have introduced fuzzy set theory to data mining. Such kind of association rules is called fuzzy association rules. Using fuzzy set concept the discoved rules are more understandable to a human. However, there are some problems in mining model, including unreasonable support definition and subjectively determining membership function. Conceretly, the main work includes:1. Firstly, based on the novel support measure and Apriori algorithm, a new algorithm for mining fuzzy association rules is proposed. Experimental results illustrate the algorithm is more effective.2. Secondly, a method of determining the membership function of sample data based on the fuzzy c-means is presented. The method overcomes the defect of determining membership functions subjectively. The method is more practical especially when it is difficult to know a priori which fuzzy set will be the most suitable.3. Thirdly, examine the relationships between mined result and the minimum support value when using different fuzzy t-norms, and analysis the influence of different t-norms on the algorithm’s performance.更多还原