【作者】 姜保庆；

【导师】 徐扬；

【作者基本信息】 西南交通大学 ， 交通信息工程及控制， 2005， 博士

【摘要】 数据挖掘是智能信息处理领域中一个十分活跃的前沿性研究方向,在许多领域均有成功的应用范例。关联规则是数据挖掘中最为热门的研究课题之一。本文引入了一种特殊的数量关联规则称为弱比例规则(Weak Ratio Rules),主要从模型、性质、挖掘、推理和应用五个方面展开对弱比例规则的研究,取得了如下研究成果: 1.讨论了一个模糊集对另一个模糊集的Goguen包含度的性质并引入了比Goguen包含度能更好描述模糊集包含程度的支持度概念,然后将模糊集的支持度概念推广到[0,+∞]值模糊集。作为[0,+∞]值模糊集支持度的特例,有限集上非负实值函数的支持度,被用来描述弱比例规则。 2.设计了挖掘有限个有限链直积下集的GenApriori算法和Boundary算法。GenApriori算法是R.Agrawal的Apriori算法的推广,是一个宽度优先算法,Boundary算法是一个深度优先算法。算法分析和实验结果均表明:在一些情况下Boundary算法优于GenApriori算法,而在另一些情况下GenApriori算法优于Boundary算法。两种算法均被用于挖掘拟极大弱比例规则。 3.指出弱比例规则问题是布尔关联规则问题的推广,是数量关联规则问题的特例。证明了任意一个弱比例规则都可诱导出一个布尔关联规则作为其支撑规则。 4.给出了弱比例规则的两种不确定性推理方法及其直观意义。 5.将弱比例规则及其推理方法应用到重构丢失数据、预测和异常值检测中,取得了较好效果。更多还原

【Abstract】 Data Mining is a very active research frontier of intelligent information processing. It has successful applications in many areas. Mining association rules is one of the most attractive research subjects of data mining. In this paper, a special quantitative association rules called Weak Ratio Rules (simply WRR) is proposed. The study on WRR consists of five parts: model, property, mining, reasoning and application. The main research results are as follows:1. The properties of Goguen inclusion degree are discussed, and a new concept support degree is presented which can describe the degree of inclusion of two fuzzy subsets better than Goguen inclusion degree. The support degree of two fuzzy subsets is generalized to that of two [0, +∞]-valued fuzzy subsets. As a special case of the support degree of two [0, +∞]-valued fuzzy subsets, the support degree of two nonnegative real-valued functions on a finite set is used to describe weak ratio rules.2. Two algorithms, GenApriori and Boundary which functions are to find all elements of a down-set of direct product of finite finite-chains, are designed. GenApriori is a generalization of R.Agrawal’s Apriori algorithm. GenApriori is a breadth-first search algorithm, but Boundary is a depth-first search algorithm. Algorithm analyses and experiments show that Boundary is more efficient than GenApriori in some cases and GenApriori is more efficient than Boundary in some other cases. The two algorithms are all used to mine quasi-maximal WRR.3. It is obtained that the WRR problem is a generalization of Boolean association rules problem and a specilization of quantitative association rules problem. It is proved that every WRR can induce a Boolean association rules as its support rule.4. Three WRR uncertainty reasoning methods and their intuitive meanings are introduced.5. WRR is applied to reconstructing lost data, forecasting and outlier detection. Experiments show that the application is successful.更多还原