摘要
从数据挖掘的一般性定义入手，给出了ＥＩＳ环境下数据挖掘的概念和过程．并根据ＥＩＳ和数据挖掘各自的特点，以概念树算法和决策树算法为例，在分析了它们的算法原理的基础上，探讨了通过属性值间概念存在的层次关系实现ＥＩＳ数据查询的逐级细化；根据信息论原理，以分类学习为基础，通过计算各属性所含信息量大小，得出判断规则，为ＥＩＳ辅助决策提供支持．
Beginning with the general definition of Data Mining, the concept and process of Data Mining in the environment of EIS are given. According to the features of EIS and Data Mining, two algorithms namely concept tree algorithm and decision tree algorithm are selected and used in EIS. Concept tree algorithm is good to provide the detailed data and summarized data in EIS by analyzing the concept levels of each attribute. Decision tree algorithm is used to get decision rules that are important for decision support in EIS, by means of computing each attribute's information quantity, based on information theory and classification learning.
出处
《华中理工大学学报》
CSCD
北大核心
1998年第5期78-80,共3页
Journal of Huazhong University of Science and Technology
基金
国家自然科学基金
