EIS 环境下的数据挖掘技术的研究  

The Technology of Data Mining in EIS

作　　者：高波[1] 陈学广[1] 费奇[1] 

机构地区：[1]华中理工大学系统工程研究所

出　　处：《华中理工大学学报》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

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.

关 键 词：数据库 管理信息系统 EIS 数据挖掘 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP315[自动化与计算机技术—计算机科学与技术]