一种基于频繁k元一阶元规则的多维离散数据挖掘模型
Research on Frequent k-ary Meta Rule in First Order for Multi-dimensional Discrete Data Mining
作者：曾涛(四川大学 计算机学院, 四川 成都 610065)；唐常杰(四川大学 计算机学院, 四川 成都 610065)；向勇(四川大学 计算机学院, 四川 成都 610065)
Author：(School of Computer Sci., Sichuan Univ., Chengdu 610065, China)；(School of Computer Sci., Sichuan Univ., Chengdu 610065, China)；(School of Computer Sci., Sichuan Univ., Chengdu 610065, China)
收稿日期：2006-06-27 年卷（期）页码：2007,39(5):121-126
期刊名称：工程科学与技术
Journal Name：Advanced Engineering Sciences
关键字：数据挖掘; 元规则; k元一阶元规则; 多维; 离散数据
Key words：data mining; meta-rule; k-ary meta rule in first order; multi-dimensional;discrete data
基金项目：国家自然科学基金项目（60473071; 90409007）; 四川省教育厅资助科研项目（2006B067）
中文摘要
为实现对多维离散数据的挖掘，提出了包含“与”、“或”、“非”逻辑的元规则概念模型，定义了元规则实例及相应的支持度和置信度概念。在此基础上提出了新的更精炼且更有启发意义的k元一阶元规则概念模型，定义了频繁度概念，证明了k元一阶元规则的空间性质定理包括上下界计算公式。文中的元规则具有更高的抽象层次，更小的解空间，能够描述元数据间的关系以及强规则实例的分布的情况。给出了k
英文摘要
To process multi-dimensional discrete data, formal concept of meta rule including connective “AND” “OR” or “NOT” was proposed. Support degree and confidence degree of meta-rule instance were defined. Solution space of meta rule problem was analyzed. Furthermore, formal concept of frequent k-ary Meta Rule in First Order (k-MR) was introduced. The concept of frequent degree and the bound equation of solution space of k-MR were presented. The k-MR, with smaller solution space, is more abstract than its base rule. It can represent distribution of strong meta rule instance and relationship between meta data. Space distribution of k-MR was also studied and verified in experimental evaluation where k
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