用语言云模型发掘关联规则(英文)  

Mining Association Rules with Linguistic Cloud Models

作　　者：李德毅[1] 邸凯昌[2] 李德仁[2] 史雪梅 

机构地区：[1]中国电子系统工程研究所,北京100036 [2]武汉测绘科技大学信息工程学院,武汉430070 [3]香港理工大学计算机系

出　　处：《软件学报》2000年第2期143-158,共16页Journal of Software

基　　金：国家自然科学基金! ;No.496 310 5 0;测绘遥感信息工程国家重点实验室基金!WKL (97) 0 30 2 )

摘　　要：该文提出用语言云模型用于 KDD中知识表达和不确定性处理 ,引入了多维云模型作为一维模型的扩展 .语言云的数字特征量将语言值的模糊性和随机性用统一的方式巧妙地综合到一起 ,基于云模型的概念层次结构可以跨越定量和定性知识之间的鸿沟 .为了发现强关联规则 ,属性值要在较高的概念层上泛化 ,同时允许相邻属性值或语言项间有重叠 .这种软划分可以模仿人类的思想 ,使发现的知识具有稳健性 .将基于云模型的泛化方法与 Apriori算法结合起来 ,从空间数据库中发掘关联规则 .试验显示了其有效性、高效性和灵活性 .This paper presents linguistic cloud models for knowledge representation and uncertainty handling in KDD. Multi dimensional cloud models are introduced as the extension of one dimensional ones. The digital characteristics of linguistic clouds well integrate the fuzziness and randomness of linguistic terms in a unified way. Conceptual hierarchies based on the models can bridge the gap between quantitative knowledge and qualitative knowledge. In order to discover strong association rules, attribute values are generalized at higher concept levels, allowing overlapping between neighbor attribute values or linguistic terms. And this kind of soft partitioning can mimic human being's thinking, while making the discovered knowledge robust. Combining the cloud model based generalization method with Apriori algorithm for mining association rules from a spatial database shows the benefits in effectiveness, efficiency and flexibility.

关 键 词：语言云模型 关联规则 知识发现 空间数据库 

分 类 号：P9[天文地球—自然地理学] TP392[自动化与计算机技术—计算机应用技术]