【摘要】 大型的数据库和数据仓库中的数据往往是有噪声和不一致的,应用经典的粗糙集理论对其进行数据挖掘处理时,效果不够理想.引入信息颗粒的概念,给出了属性子集引导的信息颗粒的构造方法及基于信息颗粒的知识描述,并应用粗糙集的扩展模型讨论知识的粗糙度问题,提出了基于粗糙信息颗粒的属性约简算法,该算法在给定最小置信度阈值的情况下,可实现对不一致数据集的简洁知识提取.图1,表2,参8.更多还原

【Abstract】 Data mining refers to extracting interesting information or patterns from data in large databases.Noisy and inconsistent data are commonplace properties of large database and data warehouses.It is difficult when Noisy and inconsistent data are mined by using classical rough set theory.In this paper,the concept of information granule is introduced.Then the knowledge possessing given confidence is described by using concept of information granule and the roughness of knowledge is discussed by using extension of rough set theory.At last,the algorithm for attribute reduction based on rough information granule is presented.The example shows that the algorithm in this paper is an effective one to extract simplicity knowledge from noisy and inconsistent data when the minimum confidence threshold is given.1fig.,2tabs.,8refs.更多还原