摘要
将不完全数据分为了两类:属性值残缺和属性值隐含。对基于这两类不完全数据的数据挖掘方法分别进行了探讨,给出了相应的处理方法,并对这些方法及其应用进行了讨论。属性值残缺的处理主要采用一系列“补漏”的方法,使数据成为完全数据集;属性值隐含的处理则通过EM算法来优化模型的参数,弥补数据的不完全性。
It is divided into two classes for incomplete data:The attribute values missing and the attribute values concealed.The data mining methods based on these two kinds of incomplete data are explored.The methods to process these two kinds of incomplete data are presented and the applications about these methods are discussed.Some prosthesis methods are used to process the attribute values missing situation and make the data complete.The EM algorithm is used to process the attribute values concealed situation and make the model parameters more suitable.
作者
吴新玲
WU Xin—ling(Department of Information Engineering,GuangDong Polytechnic Normal University,Guangzhou 510262,China;State-key Lab of Software Engineering,Wuhan University,Wuhan 430072,China)
出处
《计算机工程与设计》
CSCD
北大核心
2006年第9期1557-1559,共3页
Computer Engineering and Design
基金
武汉大学软件工程国家重点实验室开放基金资助项目(SKLSE05-09)
