| 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. |
| 关键词: 语言云模型,关联规则,Apriori算法,虚拟云,空间数据发掘. |
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| 基金项目:The research is supported by the National Natural Science Foundation of China(国家自然科学基金,No.49631050)and by the National Laboratory for Information Engineering in Surveying,Mapping and Remote Sensing(测绘遥感信息工程国家重点实验室,WKL(97)0302). |
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| Mining Association Rules with Linguistic Cloud Models |
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LI De-yi,DI Kai-chang,LI De-ren,SHI Xue-mei
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| Abstract: |
| 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. |
| Key words: Linguistic cloud model,association rules,Apriori algorithm,virtual cloud,spatial data mining. |
